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The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas

Chenglei Si, Tatsunori Hashimoto, Diyi Yang

arXiv:2506.20803v1cs.CLcs.AIcs.CYcs.HCcs.LG

TL;DR

Prior evaluations often judged LLM-generated research ideas without examining what happened when they were executed. This study has expert researchers execute randomly assigned human- and LLM-generated ideas and blindly reviews the projects. LLM ideas show significantly larger score declines across the shared metrics, despite having higher pre-execution scores.

  • Problem

    Prior evaluations mostly assess ideas without execution outcomes, although idea quality is difficult to judge even for experts and execution-based assessment is resource- and time-consuming.

    Source: §1 · pp. 1–2

  • Method

    Forty-three expert researchers execute randomly assigned human- or LLM-generated ideas, and expert reviewers blindly assess the resulting projects against prior ideation evaluations.

    Source: §2.1 · p. 3, §1 · p. 3

  • Results

    AI ideas’ scores drop significantly more than human ideas’ on novelty, excitement, effectiveness, and overall after execution, despite higher pre-execution scores.

    Source: §1 · p. 3, §4.2 · p. 7

  • Takeaways & Limitations

    The findings highlight the difficulty of evaluating research ideas without execution outcomes and support considering execution outcomes when evaluating AI-generated ideas.

    Source: §1 · p. 3, §7 · pp. 11–12

    The ideas were constrained to novel prompting techniques, so the findings may not generalize to research requiring complex modeling or large-scale data collection or training.

    Source: §7.2 · p. 12

Abstract

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Large Language Models (LLMs) have shown promise in accelerating the scientific research pipeline. A key capability for this process is the ability to generate novel research ideas, and prior studies have found settings in which LLM-generated research ideas were judged as more novel than human-expert ideas. However, a good idea should not simply appear to be novel, it should also result in better research after being executed. To test whether AI-generated ideas lead to better research outcomes, we conduct an execution study by recruiting 43 expert researchers to execute randomly-assigned ideas, either written by experts or generated by an LLM. Each expert spent over 100 hours implementing the idea and wrote a 4-page short paper to document the experiments. All the executed projects are then reviewed blindly by expert NLP researchers. Comparing the review scores of the same ideas before and after execution, the scores of the LLM-generated ideas decrease significantly more than expert-written ideas on all evaluation metrics (novelty, excitement, effectiveness, and overall; p < 0.05), closing the gap between LLM and human ideas observed at the ideation stage. When comparing the aggregated review scores from the execution study, we even observe that for many metrics there is a flip in rankings where human ideas score higher than LLM ideas. This ideation-execution gap highlights the limitations of current LLMs in generating truly effective research ideas and the challenge of evaluating research ideas in the absence of execution outcomes.

1 Introduction

Prior evaluations of LLM-generated research ideas often rely on LLM judges or small-scale human reviews, leaving open whether favorable judgments translate into research outcomes. This study executes randomly assigned human- and LLM-generated ideas and finds a larger post-execution score decline for LLM ideas.

Motivation

  • Earlier evaluations of research ideas mostly omit execution outcomes and rely on LLM judges or small-scale human evaluation.Even expert judgments of ideas are difficult, and prior execution studies verified only one or two AI-generated ideas.Source: §1 · pp. 1–2

Execution study and findings

  • LLM-generated ideas receive higher pre-execution scores, but their scores fall more after execution than human ideas, significantly narrowing the gap and reversing rankings.The larger decline is reported as significant; the lower execution scores for AI ideas are not statistically significant in the cited comparison.Source: Figure 2 · p. 2, §1 · p. 3
  • The study recruits 43 expert researchers to execute randomly assigned human- or AI-generated ideas, followed by blind expert review of the projects.The ideas come from a prior ideation study, enabling comparison with their pre-execution evaluations.Source: Figure 1 · p. 2, §1 · p. 3

2 Execution Study Setup

The study standardizes execution and blinded review in a randomized comparison of human- and LLM-generated research ideas. Its procedures aim to preserve the assigned ideas while limiting selection and implementation changes.

2.1 High-Level Study Design

  • Each of 43 participants is randomly assigned an anonymized human- or LLM-origin idea for a three-month project, with reviewers also blinded to its source.Projects include experiments and a four-page paper; the design and hypotheses were preregistered.Source: §2.1 · p. 3
  • Ideas span seven NLP topics, were scoped for execution within three months, and are evaluated before and after execution using prior ideation scores and 58 expert reviewers.The post-execution guidelines closely match those used in the prior ideation study.Source: §2.1 · p. 3, Table 1 · p. 3

2.2 Minimizing Confounders in the Study Design

  • Participants select preferred topics before random assignment to reduce self-selection, and may refine experimental details but not substantially change proposed methods.A project was terminated and excluded when its idea was too vague to execute without inventing method details; completed projects require code and a short paper for blind review.Source: §2.2 · p. 4

3 Execution and Reviewing Participants

The study uses recruited researchers to execute the ideas and expert reviewers to assess the resulting projects. Participants contribute substantial research experience and execution effort, while the review pool covers projects across topics.

3 Execution and Reviewing Participants

  • The study relies on qualified executors and blind expert review to assess project outcomes and execution fidelity.The participant pool spans 40 institutions across the world.Source: §3 · p. 4, §3.2 · p. 6

3.1 Expert Recruitment

  • Of 66 onboarded execution participants, 43 completed the task; 58 reviewers produced 181 reviews, with each project receiving four to five reviews.Most non-completions were attributed to personal reasons; one was due to an idea being too vague and infeasible.Source: §3.1 · p. 5

3.2 Expert Qualifications and Efforts

  • Execution participants averaged 15.3 papers in the Human condition and 14.3 in the AI condition, and spent 112.6 and 93.7 hours executing ideas, respectively.Participants reported moderately high topic familiarity, while reviewers averaged 596.8 citations and spent 52.5 minutes per review.Source: §3.2 · p. 5

4 Quantitative Results

The section compares execution review scores directly and against prior ideation scores. LLM-generated ideas show significantly larger declines across the four shared metrics, while direct execution-score comparisons depend on the unit of analysis.

4.1 Comparing Human and AI Ideas in the Execution Study

  • With reviews treated as independent observations, human ideas score significantly higher on excitement, effectiveness, soundness, and overall, but not novelty; idea-level differences are nonsignificant.The review-level analysis uses 181 reviews, whereas idea-level comparisons use 43 ideas and lack sufficient power to confirm the preregistered hypothesis.Source: §4.1 · p. 6

4.2 Measuring the Ideation-Execution Gap

  • The ideation-execution gap compares post-execution scores with ideation scores to account for heterogeneity in idea quality.Negative gaps indicate that scores decreased after execution.Source: §4.2 · pp. 6–7
  • AI ideas’ execution-minus-ideation gaps are significantly more negative than human ideas’ on novelty, excitement, effectiveness, and overall score.On a 1–10 scale, human novelty, excitement, and effectiveness gaps are −0.010, +0.078, and −0.052; AI gaps are −1.049, −1.760, and −1.879, respectively.Source: Table 5 · p. 7, §4.2 · p. 7

5 Analyzing the Ideation-Execution Gap

The analysis finds that execution changes usually preserve the original ideas while refining experimental details, but execution reviews assess more factors and expose weaknesses that ideation reviews may overlook. Randomly selected examples illustrate the executed projects and their review scores.

5.1 Changes Made to the Ideas Mainly Focus on Experiment Details

  • Human and AI ideas receive an average of 2.9 and 3.1 changes, respectively, mostly to experiment details rather than proposed algorithms.Examples include updating models, adding metrics or baselines, and specifying missing experimental details; changes generally preserve the ideas and improve experiment design.Source: §5.1 · p. 8
  • Six AI ideas replaced proposed human evaluations with automatic evaluation, but excluding them leaves the larger AI ideation-execution gap significant.The authors conclude that these changes explain only a small fraction of the gap.Source: §5.1 · p. 8

5.2 Execution Evaluation Considers More Factors Than Ideation Evaluation

  • Execution reviewers ground judgments in empirical outcomes and scrutinize experimental rigor, often surfacing weaknesses overlooked during ideation.They also consider factors such as baselines, ablations, resource requirements, and generalizability.Source: §5.2 · pp. 9–10

5.3 Randomly Selected Examples

  • The examples are randomly selected and include three human ideas and five AI ideas, with original proposals, executed papers, and review scores.Named examples include a human idea with a 5.3 average overall score after execution and an AI idea with a 5.0 score.Source: §5.3 · p. 10

6 Related Work

Prior work develops LLM methods for generating and evaluating research ideas and building AI scientists. These studies generally evaluate ideas without execution outcomes, while this paper evaluates executed projects.

Research Idea Generation and Evaluation

  • Research-idea generation work improves scaffolds through retrieval, iterative feedback or revision, and multi-agent collaboration, while some work fine-tunes specialized models.The cited examples include retrieval (Li et al., 2025, Wang et al., 2024a,b), revision (Hu et al., 2024, Yang et al., 2024), collaboration (Su et al., 2025), and fine-tuning (O’Neill et al., 2025, Weng et al., 2025).Source: §6 · p. 11
  • Most idea-evaluation studies use LLM judges, and prior work generally evaluates ideas without execution outcomes, which can differ from actual outcomes.Si et al. (2025) conducted a large-scale expert evaluation and identified limitations of LLMs as automatic judges.Source: §6 · p. 11

LLMs as AI Scientists

  • AI Scientist systems automate research pipelines through code generation, wet-lab validation, or iterative searches informed by execution feedback.The paper reports recruiting experts to execute ideas as a large-scale execution evaluation for open-ended research.Source: §6 · p. 11

7 Discussion

The discussion argues that AI-generated ideas should be evaluated using execution outcomes, while noting the study’s constrained idea scope and modest sample. It outlines ethical policies and future directions for scaling execution evaluation and using its feedback.

7 Discussion

  • The authors report large ideation-execution gaps for LLM-generated ideas and advocate considering execution outcomes when evaluating them.The study is presented as the first large-scale execution evaluation comparing LLM-generated and expert ideas.Source: §7 · pp. 11–12

7.1 Ethical Considerations

  • Executors may own and further develop implemented projects, while any public posting or venue submission must disclose the idea’s original source.The study uses compensated reviewers rather than external conference or workshop review to reduce burden and enable a controlled, balanced evaluation.Source: §7.1 · p. 12

7.2 Limitations

  • The study reuses ideas limited to novel prompting techniques, so findings may not generalize to ideas requiring complex modeling, large-scale data collection, or training.The 43-project sample may also limit power for granular analyses and increase sensitivity to executor variability.Source: §7.2 · p. 12

7.3 Future Work

  • Future work could develop research agents that execute ideas near human quality, reducing reliance on expert labor for large-scale evaluation.The paper notes current systems’ low reliability and poor generalization on complex, open-ended research execution tasks.Source: §7.3 · pp. 12–13
  • Proxy reward models could estimate idea effectiveness without full implementation, using historical execution outcomes or simulated execution environments.The paper suggests using such models to rank or filter ideas and as reward functions for idea-generation training.Source: §7.3 · p. 13
  • Execution feedback could guide iterative idea improvement through evolutionary search, self-refinement, reinforcement learning, or fine-tuning.The proposed loop uses outcomes from executed experiments as feedback or reward signals.Source: §7.3 · p. 13

A Full Review Form

This appendix documents the review instruments, participant information, LLM choice for masked-prompt generation, score-distribution and correlation displays, and examples of changes made during execution, including refinements to evaluation metrics, hyperparameters, baselines, analyses, and prompt details.

A Full Review Form

  • Reviewers had one week and scored novelty, excitement, soundness, effectiveness, codebase quality, and overall quality using the provided review form.The form also requested rationales for scores; overall scoring was calibrated to a four-page *ACL short paper.Source: A Full Review Form · p. 18, §9 · pp. 18–21

4. Consent

  • Reviewers were asked not to use AI tools when writing reviews and to report topic familiarity and prior reviewing experience.Topic familiarity ranged from no prior reading to multiple publications, while experience questions referenced major NLP or AI conferences.Source: §4 · p. 18

9. Executed Codebase

  • The review form also assessed faithfulness to the proposed outline and reviewer confidence, with defined rating scales and rationales.Faithfulness concerned alignment between the final paper and outline; confidence was rated from 1 to 5.Source: §9 · pp. 21–22

B Institutions; C Plots; D Correlation

  • The appendix lists institutions for 43 execution participants and 58 reviewers, plots execution-evaluation scores by condition, and reports mostly weak ideation–execution correlations, including a moderately negative AI-idea excitement correlation.The score-distribution figure shows boxplots with means and 95% confidence intervals; the correlation table includes Pearson and Spearman coefficients.Source: Table 7 · p. 23, Table 8 · p. 24, Figure 4 · p. 25

E Changes Made to the Ideas

  • Execution participants changed proposed datasets and models, including removing datasets that lacked required metadata and replacing older models with newer ones.Examples include adding Jailbreak-Bench and DAN-Forbidden-Questions, and changing a human idea from masked language models to autoregressive models.Source: E Changes Made to the Ideas · p. 27
  • Executors replaced factual accuracy with detected bias rate as the main metric for an AI-generated debiasing idea.The example is “Adversarial Stereotype Dissolution Prompting.”Source: E Changes Made to the Ideas · p. 27
  • Executors added or modified hyperparameters such as LLM sampling settings, method iterations, in-context examples, and performance-reporting runs.These parameters were decided or adjusted across multiple projects.Source: E Changes Made to the Ideas · p. 27
  • Executors added or changed baselines, including model-based-embedding RAG, newer SWE-Bench baselines, and LLM-prompting baselines for parsing.These additions addressed missing or updated comparison methods in the experiment plans.Source: E Changes Made to the Ideas · p. 27
  • Executors dropped Monte Carlo Dropout from an AI idea’s baselines because it was infeasible with black-box LLMs.The idea was “Entropy-Guided Prompt Mutation.”Source: E Changes Made to the Ideas · p. 27
  • Executors added analysis or ablation experiments, including tests of hierarchical decomposition and multi-perspective verification and performance-cost trade-offs.These examples came from human ideas.Source: E Changes Made to the Ideas · p. 27
  • Executors specified missing prompts or instantiated steps into concrete prompts, refining planned procedures rather than creating new steps.Examples include chunk-relevance scoring and a prompt for generating a source-sentence paragraph before translation.Source: E Changes Made to the Ideas · pp. 27–28

A.2 Examples

  • The summary-evaluation prompt scores coherence, fluency, relevance, consistency, and conciseness on a 1–5 scale; its example reports expected effectiveness of 5.0.The evaluation steps instruct reviewers to compare the summary with the source document and assign a score for each metric.Source: §A.2 · pp. 50, 52

A.1 Prompts for Masked-Dataset Generation

  • The masked-dataset-generation developer prompt asks for potentially harmful semantic categories relevant to a prompt.The accompanying figures are identified as prompts for category generation and span highlighting by GPT-4o.Source: §A.1 · p. 118, Figure 4 · p. 118, Figure 5 · p. 118

A.2 Choice of LLM for Masking

  • GPT-4o generated categories and highlighted spans for 90.63% of harmful prompts, while Vicuna’s inconsistent output and Llama’s safety behavior limited their utility.GPT-4o was used with default API parameters, temp=1.0 and top_p=1.0.Source: §A.2 · p. 118

F Results of Excluding the 6 AI Ideas that Involve Human Evaluation Removal

Excluding the six AI ideas whose human-evaluation proposals executors removed does not change the finding that AI ideas have larger ideation-execution gaps.

F Results of Excluding the 6 AI Ideas that Involve Human Evaluation Removal

  • After excluding six AI ideas with removed human-evaluation proposals, AI ideas still drop significantly more than human ideas on all four shared metrics.Negative gaps indicate score decreases; p-values are FDR-adjusted.Source: Table 10 · p. 29

G Correlation Between Different Metrics

Overall scores correlate strongly with the component metrics, and changes in overall scores correlate highly with changes in novelty, excitement, and effectiveness.

G Correlation Between Different Metrics

H.1 Example 1: A Compound LLM System to Mimic Knowledge Unlearning in Large Language Models

  • The example proposal asks whether prompting alone can mimic fine-tuning-based unlearning and outlines a compound LLM system with specialized roles.Its stated testbed is WMDP.Source: §H.1 · p. 31

Abstract

  • The abstract reports lower accuracy on dangerous-knowledge queries, high-quality responses on unrelated queries, and better safety and unlearning fidelity than existing baselines.The abstract names WMDP, TOFU, and Who’s Harry Potter among the evaluated benchmarks.Source: Abstract · p. 33

1 Introduction

  • The introduction frames unlearning as removing specific knowledge, notes limits of fine-tuning approaches, and identifies WMDP as the evaluation benchmark.It presents a compound architecture and claims safety and knowledge suppression while retaining utility on nontarget queries.Source: §1 · p. 33

2 Proposed Method

  • The proposed inference-time pipeline identifies forbidden-topic queries and suppresses sensitive information while preserving answers to legitimate queries.The authors motivate multiple specialized components as protection against weaknesses in a single prompt or model instance.Source: §2 · p. 34

2.1 System Architecture

  • The system routes unrelated queries to a responder and related queries to a deflector, then filters the response and requests a safer resample if needed.The authors describe modular checks as layered defenses against probes and jailbreaks.Source: §2.1 · p. 34

2.2 Prompt Design and Optimization

  • Prompt design and DSPy optimization refine the orchestrator’s topic-detection prompts by testing variants on labeled related-or-unrelated queries.The best-performing prompt design is retained.Source: §2.2 · pp. 34–35

3.1 Models; 3.2 Benchmarks; 3.3 Baselines; 3.4 Implementation Details

  • The evaluation uses open-source and proprietary models, three unlearning benchmarks, prompting, guardrail and fine-tuning baselines, and separate GPT-4 sessions or Python for components.The benchmarks include WMDP, Who’s Harry Potter, and TOFU; utility is also assessed on retain or unrelated queries.Source: §3.1 · p. 35, §3.2 · p. 35, §3.3 · p. 35

4 Results

This section presents the evaluation setup and results for the compound system, including its targeted-knowledge suppression, retained utility, prompt optimization, baselines, and challenges applying ACP to open-weight models.

4.1 Main Results

  • Restricted-query accuracy was lowest with the proposed method at Cyber 24.6%, Bio 26.3%, and Chem 27.2%, outperforming RMU and prompting-based baselines.Lower restricted-query accuracy indicates better suppression on WMDP.Source: §4.1 · p. 36
  • The method retained 58.4% accuracy on MMLU and scored 7.57 on MT-Bench, indicating preserved general-knowledge performance and conversational fluency.The base model was Llama-3-8B.Source: §4.1 · p. 36
  • WHP yielded 297/300 (99.0%) sensitive queries with deflected answers, while TOFU results were 39/40 (97.50%), 194/200 (97.00%), and 389/400 (97.25%) for forget 1, 5, and 10.The passage reports significantly lower WHP familiarity scores than standard prompting, but gives no numerical familiarity scores.Source: §4.1 · pp. 36–37

4.2 Ablation Results

  • Prompt optimization reduced restricted-query accuracy from 31.7% to 24.6% for Cyber, 32.0% to 26.3% for Bio, and 35.8% to 27.2% for Chem.The flagged ratio also rose from 67.1%-87.6% without optimization to 97.4%-99.1% with optimization, strengthening routing of restricted queries to the deflector.Source: §4.2 · p. 37
  • Across model variants, Llama 3-8B had slightly lower MMLU utility, Qwen-2.5-72B was less consistent across categories, DeepSeek variants balanced suppression and utility, and optimized GPT-4o had the best trade-off.The results describe the approach as robust across models but say performance varies with architecture and alignment properties.Source: §4.2 · p. 37

3.1 Dataset

  • Govreport’s maximum word count can exceed 30,000 words, often surpassing the context limits of open-weight models.Table 1 reports word-count statistics for the source and target texts.Source: §3.1 · p. 45

3.2 Models

  • Open-weight Llama 3.1 and InternLM-7B models often failed ACP’s structured-output requirements, causing suboptimal performance or infinite loops and limiting experiments to GPT-4o-mini.The main experiment uses GPT-4o-mini for coherent, contextually relevant generation at a cost-effective scale.Source: §3.2 · p. 45

3.3 Baselines

  • The baselines are fixed-window truncation, overlapping sliding windows, and full-window processing, which uses the entire input without truncation.Fixed windows can discard information, sliding windows can carry irrelevant content across chunks, and full-window processing can be inefficient or unsupported by some models.Source: §3.3 · p. 45

3.4 Evaluation Metrics

  • Evaluation combines ROUGE, BERTScore, compression ratio, and factual consistency with LLM scores for coherence, fluency, relevance, consistency, and conciseness.The LLM-based evaluation rates each aspect on a 1–5 scale.Source: §3.4 · p. 45

3.5 Prompts

  • Prompts are zero-shot; baseline summaries use a simple prompt, while ACP uses dedicated prompts for relevance scoring, pruning, and retrieval.Sliding-window summaries are merged with a final prompt, and LLM-based evaluation adapts a prompt inspired by Liu et al. (2023).Source: §3.5 · pp. 45–46

4 Results and Ablation

The supplied results report ACP’s competitive performance on long-form summarization and describe a separate self-improving-memory approach with reported gains on mathematical reasoning.

4 Results and Ablation

  • ACP scores 27.24 on ROUGE-1, slightly above Full Window’s 27.03, and achieves competitive BERTScore precision (86.76) and F1 (84.97).Its automatic-evaluation results are described as close to full-context results.Source: §4 · p. 46
  • ACP scores 4.81 in relevance, above Full Window (4.75) and Sliding Window (4.68), while coherence, fluency, consistency, and conciseness remain competitive.All methods have similar coherence and fluency scores; ACP scores 4.00 in coherence and 4.60 in fluency.Source: §4 · p. 46

5 Conclusion

  • On GOVREPORT, ACP performs comparably to or better than three window-based methods in relevance and conciseness, with competitive factual consistency and readability.The conclusion identifies retrieval heuristics and hybrid retrieval-augmented models as future work.Source: §5 · p. 46

H.3 Example 3: Self-improving Memory Ignites Mathematical Reasoning for Large Language Models

  • The proposed memory pipeline decomposes problems, stores development-set subtask–solution pairs, then retrieves related units or generates similar tasks at test time.The same LLM performs each step through different prompts.Source: §H.3 · p. 53

Abstract

  • The self-improving-memory framework stores solved subtasks and retrieves or approximates relevant examples at inference; reported accuracy gains are strongest on multi-step problems.The abstract reports evaluations on GSM8K, MATH, and MMLU-Math, and says retrieval granularity affects performance and answer preference.Source: Abstract · p. 55

1 Introduction

  • The paper reports accuracy improvements across math tasks, especially multi-step reasoning, using benchmarks including GSM8K, MATH, and MMLU-Math.The introduction names GPT-3.5, GPT-4, and LLaMA3 among the evaluated models.Source: §1 · p. 55

3 Experiments

  • For same-distribution tests, direct methods slightly outperform decomposed methods; direct accuracy also tends to remain higher on unseen distributions.The experiments cover over 10,000 samples across three benchmarks, using training problems for memory construction and test problems for evaluation.Source: §3 · p. 57, Table 2 · p. 57, §3.1 · p. 57

3.2 Evaluation Protocol

  • Changing the source dataset for static memory significantly affects downstream performance, especially on out-of-distribution tests.Dataset choice is presented as a way to control memory-unit granularity and topical specialization.Source: §3.2 · p. 57

4 Experimental Results and Analysis

The section presents experimental analyses of QA systems, including DCM’s graph-structure-based adjustment of raw confidence on contrastive question variants from four datasets, evaluated for accuracy, calibration, and selective-prediction performance. It also examines direct answering, decomposition, and memory, considering domain shift, retrieval granularity, reasoning failures, and answer preferences.

4.1 Results on Training Distribution

  • Direct answering slightly outperforms decomposition-based methods in-distribution across models, with the gap narrowing for stronger models such as LLaMA3-70B.Source: §4.1 · p. 58

4.2 Cross-Dataset Generalization

  • Under domain shift, direct methods generally retain stronger performance, while decomposed methods suffer more significantly.Source: §4.2 · p. 58

4.3 Effect of Memory Granularity

  • For LLaMA3-70B on MATH, k=5 yields the highest decomposed accuracy (0.91), while the discussion says moderate k values such as 3 or 4 give the best performance and human preference.Smaller k values (1–2) yield strong direct-answer performance but limited memory gains; decomposed accuracy improves as k increases from 3 to 5.Source: §4.3 · pp. 58–59, §4.5 · p. 59

4.4 Answer Quality and Preference

  • Direct answers are preferred across most settings for their clarity, although decomposed answers sometimes offer richer detail.The discussion identifies improving how retrieved subtasks are integrated into final answers as a future direction.Source: §4.4 · p. 59, §4.5 · p. 59

4.5 Discussion

  • Memory-augmented reasoning offers modest benefits, particularly for LLaMA3-70B on in-distribution data, and decomposition may generalize poorly because of subtask mismatch or shallow matching.Source: §4.5 · p. 59

5 Further Analysis

  • Failure analysis finds over-decomposition creates redundant subtasks for enumeration, while multi-fact reasoning exposes abstraction and dependency limitations.The paper suggests better granularity control, stronger decomposition priors, or multi-step dependency modeling to address these limitations.Source: §5 · p. 59

3.3 Confidence Graph Construction

  • The confidence graph adds weighted edges between semantically similar questions and uses 64-dimensional Node2Vec embeddings to represent its confidence landscape.The graph combines confidence ranking with semantic clustering; similar-question edges use Sentence-BERT cosine similarity.Source: §3.3 · p. 67, §3.4 · p. 67, §4.5 · p. 68

3.5 Confidence Calibration

  • DCM subtracts a structure-aware correction from raw confidence to penalize overconfident predictions, then logistic calibration maps the adjusted score to correctness probability.The logistic regression is trained on a held-out validation set.Source: §3.5 · p. 67

4.1 Datasets

  • The unified dataset combines CommonsenseQA, TriviaQA, MMLU, and SciQ with mild, moderate, hard, and domain-shifted contrastive variants, plus held-out queries.The held-out queries evaluate calibration on unseen inputs.Source: §4.1 · pp. 67–68

4.2 Contrastive Prompt

  • Gemini-1.5-Flash generates contrastive prompts with few-shot demonstrations and chain-of-thought reasoning (Wei et al., 2022), alongside temperature-scaling and ensemble-agreement baselines.Temperature scaling maps raw GPT-4o confidence to correctness probabilities; the ensemble baseline estimates confidence from agreement across stochastic responses (Guo et al., 2017; Desai and Durrett, 2020; Wang et al., 2022).Source: §4.2 · p. 68, §4.3 · p. 68

4.4 Evaluation Metrics

  • Evaluation reports exact-match accuracy, Brier Score, ECE, reliability diagrams, and AUC for the accuracy–coverage trade-off, with lower Brier and ECE indicating better calibration.AUC assesses selective prediction across varying coverage thresholds without requiring fully calibrated probabilities (Kamath et al., 2020).Source: §4.4 · p. 68

4.5 Hyperparameters

  • Experiments use decoding temperature T = 0.5 for GPT-4o and Gemini-1.5-Flash, validation-tuned α values of 1000 and 1100, and k = 30 nearest neighbors.Unless otherwise specified, Node2Vec embeddings have 64 dimensions.Source: §4.5 · p. 68

5 Results and Ablation

This section reports comparative results and ablations across the evaluated methods and settings. It examines how performance varies with method and configuration choices, auxiliary-language translation quality, prompting, and evaluation approach, including accuracy–coverage trade-offs.

5.1 Main Results

  • Temperature scaling has the lowest ECE and Ensemble the lowest Brier Score and slight accuracy edge; DCM reaches ECE 0.006 at α = 1100 and highest AUC at α = 1100 and α = 1200.Reliability diagrams show Temperature closest to the ideal curve, while DCM aligns more smoothly in higher-confidence bins and Ensemble deviates more in lower-confidence regions.Source: Table 1 · p. 68, §5.1 · pp. 68–69

5.2 Ablation Setup

  • Using 3 contrastive variants instead of 4 yields ECE 0.006 vs. 0.007 and AUC 0.8 vs. 0.6, with similar accuracy and Brier scores.The ablation excludes the hard variant to test the effect of difficulty diversity in contrastive prompting.Source: §5.2 · p. 69
  • Increasing Node2Vec dimensions from 64 to 128 improves accuracy and Brier Score and raises AUC to 0.8 from 0.6, but worsens ECE to 0.009 from 0.007.This ablation varies embedding dimensionality to assess graph representation capacity.Source: §5.2 · p. 69

3.2 Metrics

  • Effective accuracy combines answer accuracy with answer coverage, addressing a composite-accuracy metric that can reward answering just one question correctly.The authors use a weight-less coverage formulation despite its inherent bias toward answering rather than abstaining; reported accuracy is effective accuracy unless noted otherwise.Source: §3.2 · pp. 77–78

Model Performance and Optimal Confidence

  • The pipeline selects each model’s optimal confidence cutoff by averaging accuracy across its MKA runs; results show different models peak at different cutoffs.The cutoff candidates span 0 to 1 in increments of 0.02, forming 50 categories.Source: §3.1 · p. 77, Model Performance and Optimal Confidence · p. 78
  • Aya Expanse 8B and Gemma2 27B perform best overall, but no model dominates across auxiliary-language sets; gains depend more on auxiliary languages and translation quality than target language.Gemma2 27B usually performs better with mid- and high-resource auxiliary sets, while Gemma2 9B and Aya Expanse 8B lead in four configurations each.Source: Model Performance and Optimal Confidence · p. 78
  • Bengali’s best MKA accuracy is 71.2% better than its best baseline, while Yoruba’s is 24.6% lower; four languages benefit and two do not.The table covers effective accuracy on 200 examples; the reported comparison uses each language’s best MKA and baseline performance.Source: Model Performance and Optimal Confidence · p. 78, Table 2 · p. 79
  • Prompting instructions make pipeline performance volatile, particularly for Qwen-2.5 7B.The authors suggest this volatility may be related to the models themselves.Source: Model Performance and Optimal Confidence · p. 79

5 Conclusion

  • With good machine translation, MKA can improve accuracy by abstaining when the model lacks confidence, supporting confidence calibration with multilingual knowledge as a tool against hallucination.The pipeline relies on cosine similarity between sentence embeddings to judge answer correctness; future work could use commercial LLMs to evaluate equivalence or use multilingual knowledge internally to avoid translation artifacts and post-inference techniques.Source: §5 · pp. 79, 81, §6 · p. 81

Model Performance and Optimal Confidence

  • Higher answer coverage is associated with lower answered accuracy, especially for low-resource auxiliary languages, where poorer translation degrades prompts.Across resource levels, higher-resourced target languages perform well across prompting models; low-resource targets remain below par but can still exceed baseline performance.Source: Model Performance and Optimal Confidence · pp. 78–79, §5 · p. 80

B MKA Pipeline Output Example

  • In the illustrated high-resource auxiliary-language example, strong confidence and cross-language agreement lead the pipeline to answer, with high similarity to the ground truth.The example uses English as the target and German, English, French, Spanish, Simplified Chinese, and Portuguese as auxiliary languages.Source: Table 3 · p. 84

H.6 Example 6: Adversarial Scenario Extrapolation: Improving Language Model Robustness through Self-Generated Defense Strategies

This section examines prompt-based approaches—Adversarial Scenario Extrapolation (ASE), Conceptual Pivot Prompting (CPP), and Adaptive Semantic Masking (ASM)—for influencing language-model safety, bias, and harmful outputs. It reports benchmark and ablation results, with effects varying by model, dataset, and prompt and sometimes trading off against task performance or enabling jailbreaks.

1 Introduction

  • Existing defenses often target known attacks and can miss novel or subtly varied inputs, motivating ASE’s proactive generation of hypothetical attack scenarios and defenses.The introduction describes adversarial training and static rule-based systems as limited against unforeseen attack vectors.Source: §1 · p. 92

2 Proposed Method: ASE

  • ASE has four prompting steps: generate hypothetical attack scenarios, analyze their harms and techniques, devise defenses, then apply those insights when answering the original query.The final response is instructed to note the defensive measures it takes.Source: §2 · p. 93

3.1 Models; 3.2 Datasets

  • The experiments use GPT-4o and Llama 3.3 70B across 100 toxic prompts, 20,000 jailbreak prompts, and 437 adversarial TruthfulQA questions.The datasets are Real-Toxicity-Prompts, JailBreakV-28k, and TruthfulQA, respectively.Source: §3.1 · p. 93, §3.2 · p. 93

3.3 Toxic Prompt Completion

  • ASE significantly reduced toxicity, obscenity, and insult scores for both models; GPT-4o’s scores fell by around 18–34%, while rejection counts remained nearly unchanged.The evaluation used Toxic-BERT scores on 100 toxic prompts.Source: §3.2 · p. 93, §3.3 · pp. 93–94

3.4 Jailbreak Attack

  • Successful jailbreaks fell 93% for GPT-4o and 95% for Llama under ASE; the abstract also reports up to 95% fewer harmful outputs and superiority to Constitutional AI.The study evaluates jailbreak attacks using 20,000 prompts.Source: Abstract · p. 92, §3.4 · p. 94

3.5 Factual Hallucination

  • On 437 adversarial TruthfulQA questions, ASE raised GPT-4o accuracy from 81.0% to about 91.5% and Llama accuracy from 68.4% to about 85.5%.With ASE, GPT-4o had 400 correct and 37 incorrect responses; Llama had 372 correct and 65 incorrect.Source: Table 3 · p. 94, §3.5 · p. 94

3.6 Benchmark Study

  • The benchmark study selects Constitutional AI as a comparison defense and considers Mistral-7B-Anthropic, Mistral-7B-Grok, and pretrained Mistral-7B-v0.1.The supplied passage describes the benchmark setup but does not provide its results.Source: §3.6 · pp. 94–95
  • Toxicity was 0.138 for ASE Mistral, versus 0.316 for the best C.AI model (Anthropic) and 0.484 for baseline Mistral.ASE Mistral also had lower Obscene (0.173) and Insult (0.073) scores; rejection rates were almost the same across cases. The toxic-prompt test used 100 samples.Source: §3.6 · p. 95
  • 787 unsafe responses from ASE Mistral were fewer than the baseline’s 5,443, Anthropic’s 4,277, and Grok’s 2,176 on 8,000 jailbreak samples.ASE Mistral also produced 6,815 safe responses and rejected 398 attempts, a 5% refusal rate; it was the only compared model to actively reject a significant portion of attempts.Source: §3.6 · p. 95

3.7 Ablation

  • Removing both Steps 2 and 3 raised unsafe jailbreak responses to 178, nearly four times Original ASE, despite the highest rejection count (16,034).Removing either step individually also increased unsafe responses (62 without Step 2; 55 without Step 3); the authors report that the full method best balances safety across scenarios.Source: §3.7 · pp. 95–96, Table 6 · p. 96

3.8 Efficiency

  • ASE takes 4x the baseline response time, but improves safety without adversarial fine-tuning and is described as more flexible against novel attacks than static defenses.The paper attributes this flexibility to ASE’s dynamic nature, contrasting it with methods that depend on predefined datasets and attack-pattern knowledge.Source: §3.8 · p. 96

4 Limitations

  • The toxic-prompt evaluation used only 100 samples, and the study calls for broader datasets, additional models, and evaluation of bias and hallucination mitigation.The authors specifically suggest testing models such as Gemma and DeepSeek to broaden architectural coverage.Source: §4 · p. 96

5 Conclusion

  • ASE uses a model’s generative capabilities to anticipate adversarial scenarios, and the conclusion reports reduced harmful outputs across toxicity and jailbreak tasks.The paper frames ASE as a proactive alternative to defenses based mainly on predefined attack patterns or static rules and reports comparisons with baseline models and Constitutional AI.Source: §5 · pp. 96–97

H.7 Example 7: Conceptual Pivot Prompting: Reducing Social Biases in Large Language Models through Analogical Reframing

  • CPP reframes potentially biased concepts with analogies from unrelated domains, guiding model reasoning before it addresses the original task.The study evaluates it on BiosBias occupation prediction and StereoSet stereotype inference, using five LLMs; the datasets contain 1,000 items each.Source: Abstract · p. 101, §1 · p. 101, §2.2 · p. 103
  • CPP reduces bias in some cases without significantly harming task performance, but results depend on the analogies and model, and excessive use may favor antistereotypes.The proposed fallback plan is to refine or simplify analogies, consider combining CPP with other mitigation methods, or analyze bias–performance trade-offs if task performance declines.Source: §H.7 · p. 100, Abstract · p. 101

2.4 Hyperparameters

  • Most experiments used temperature 0 and default OpenAI API hyperparameters, but o1-mini and o3-mini used their API default temperature of 1.Source: §2.4 · p. 104

2.5 Metrics

  • BiosBias evaluation measures 28-way occupation accuracy and demographic parity difference, while StereoSet uses language-modeling score, stereotype score, and their idealized CAT combination.StereoSet metrics are defined consistently with Nadeem et al. (2021), with efficiency modifications; idealized CAT score is lms × min(ss,1−ss).Source: §2.5.1 · p. 104, §2.5.2 · p. 104

3.1 Main Results

  • On BiosBias, single-pivot CPP had little effect on accuracy, while its demographic-parity effect varied by pivot and model; on StereoSet, multiple pivots improved idealized CAT scores for every model.GPT-3.5-turbo was the only model for which all pivots reduced bias, and no pivot worked best across all models.Source: §3.1 · pp. 104, 106

3.2 Ablation: Multi-Pivot Prompt (CPP-k)

  • With multiple pivots, CPP-k decreased BiosBias accuracy and demographic parity difference as k rose, while on StereoSet it lowered language-modeling scores and shifted stereotype scores downward.For smaller models, the stereotype-score shift brought scores closer to 0.5 and raised idealized CAT scores; for GPT-4o it crossed into antistereotype preference, lowering that score.Source: §3.2 · p. 106

4 Conclusion

  • CPP showed promise for reducing bias across two datasets and five LLMs, especially when tailored to a model and dataset, but strong conditioning could induce antistereotype bias.Bias reduction could come at the cost of language-modeling performance or accuracy.Source: §4 · p. 106

Abstract

  • ASM’s adaptive semantic masking reduced harmful outputs in weaker models but could amplify jailbreaks in advanced models that infer masked content.The abstract reports this dual effect across five datasets covering harmful and benign prompts and four target LLMs; optimized masking further increased attack success on robust models.Source: Abstract · p. 111

1 Introduction

  • ASM identifies contextually harmful tokens with an LLM and selectively masks them; the study evaluates it on harmful and benign prompts using four target models.The introduction says the work covers five datasets and a masked dataset of 1084 out of 1.3K samples.Source: §1 · p. 111, Abstract · p. 111
  • ASM reduced Vicuna’s attack success rate by 60%-79% for generic harmful prompts, but raised GPT-4o’s from 0% to 13%.The authors describe ASM as a safeguard for less capable models and a way to expose vulnerabilities in more capable ones.Source: §1 · pp. 111–112

2 Related Work

  • The work identifies ASM’s dual defense-and-attack capability as previously unexamined in one framework: it shields weaker models but can aid stronger models in reconstructing concealed context.The conclusion highlights stronger-model vulnerabilities and the need for more effective safeguards.Source: §2 · p. 112, §6 · pp. 115–116

3 Proposed Method: ASM

  • ASM uses GPT-4o to generate harmful categories, highlight related prompt words, and replace them with [mask]; its static baseline masks from 377 harmful terms across 10 classes.The static keyword list is detailed in Appendix B.1.Source: §3 · p. 112, §4.1 · p. 113

3.1 Dataset Preparation:

  • Evaluation uses 1,310 original samples and 1,084 masked samples, targets GPT-4o, Llama3-8B, Llama2-7B, and Vicuna-7B, and flags judge scores above 5 as harmful.Datasets include adversarial prompts and benign prompts; GPT-4o judges response harmfulness on a 1–10 scale.Source: §3.1 · p. 113, §4.2 · p. 113, §4.3 · p. 113

5.1 Harmful Prompts

  • Vicuna’s ASR falls from 27%–44% under unmasked or static prompts to 9%–25% with ASM, while masking can increase jailbreak success in stronger models.The authors attribute the increase to models such as GPT-4o inferring contextually appropriate replacements for [mask].Source: Figure 1 · p. 114, §5.1 · p. 114
  • ASM’s impact varies by dataset: its ASR effect is limited on JBB-artifacts, and it even defends against attacks on Llama-3 there.JBB-artifacts contains real red-team jailbreak prompts, whereas other datasets use comparatively simpler harmful requests; the masking amplification effect is also less pronounced on robust models for JBB-artifacts.Source: §5.1 · p. 114

5.2 Optimized Attack

  • ASM’s optimized single-category masking increases GPT-4o ASR by 26.9% on Adv-Bench, 145.54% on DAN-fq, and 359.33% on JBB-harmful.The variant creates separate masked prompts for each identified category rather than masking the union of categories.Source: Figure 2 · p. 114, §5.2 · p. 115

5.3 Benign Prompts

  • 77% of JBB-benign prompts are masked, averaging 3.74 tokens each, and ASM raises response counts from 58 to 86 for GPT-4o and 91 to 98 for Vicuna.On Natural Questions, ASM masks 1 of 100 prompts; that question was mistakenly categorized as Discrimination and Civil Rights.Source: §5.3 · p. 115

Limitations

  • ASM relies on an external LLM for prompt categorization and token highlighting, increasing computational time and cost; on seemingly harmful but benign prompts, GPT-4o judge ratings showed a 55%–56% response-quality drop.Source: Limitations · p. 116

B.2 Non-adaptive Masked Dataset

The non-adaptive masking statistics are presented in Table 4, while adaptive masking covers a much larger share of harmful prompts than static masking.

B.2 Non-adaptive Masked Dataset

B.3 Adaptive vs. Non-adaptive Masking

  • Adaptive masking covers 90.63% of harmful prompts, compared with 6.85% for non-adaptive static masking.The difference is especially significant on complex datasets such as JBB-artifacts.Source: §B.3 · p. 120

C More Details on Evaluation

The evaluation appendix includes a GPT-4o judge-model prompt and discusses why simpler response-based policies can misclassify outputs. In particular, refusal-language checks can miss harmless responses, while harmful-keyword checks can miss harmful responses that lack explicit keywords.

C.1 Prompts for Evaluation

  • Figure 6 presents the prompt used for GPT-4o as the evaluation judge.Source: Figure 6 · p. 121

C.2 Other Evaluation Policy

  • A policy based on refusal terms can misclassify responses that contain no refusal phrase but are not harmful.The authors contrast this with a policy that checks for harmful terms from the masked prompt.Source: §C.2 · p. 121
  • A harmful-term check can miss harmful responses without explicit harmful keywords, which the judge model may still flag.Source: §C.2 · p. 121

D More Results

The appendix supplies additional ASR coverage and examples of responses to harmful prompts. It includes examples labeled as harmful compliance and examples labeled as harmless refusals.

D More Results

  • Figure 7 reports ASR(%) across different LLMs for harmful prompts, without values in the supplied caption.Source: Figure 7 · p. 122

E Harmful Response Examples

  • Figures 8–13 show compliance with harmful requests: GPT-4o on Adv-Bench and JBB-harmful, and Llama2-7B, Llama3-8B, and Vicuna on Adv-Bench; Figure 13 specifically uses an optimized-harmful Adv-Bench request.Source: Figure 8 · p. 123, Figure 9 · p. 124, Figure 10 · p. 125

F Refusal Response Examples

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