HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation

Problem definition

• The paper defines hypothesis generation as producing natural-language theories or explanations for observed phenomena, distinguishing it from research ideation.Ideation primarily proposes new research directions, whereas hypothesis generation explains observations.Source: §1 · p. 2

Required capabilities

• Hypothesis generation requires inductive and abductive reasoning, abstraction and communication, and optionally synthesis with existing literature.Synthetic datasets provide control over task complexity while testing these capabilities.Source: §1 · pp. 2–3

Evaluation principles

• The benchmark prioritizes explanatory power, measuring it through practical utility and supplementing it with interestingness and hypothesis discovery rate.Novelty is treated as a separate consideration rather than a prerequisite for a useful hypothesis.Source: §1 · p. 3

Benchmark contribution

• The benchmark extends prior evaluation by testing feature discovery from unstructured observations alongside explanatory power and preliminary interestingness metrics.This distinguishes HypoBench from approaches that assume relevant features are already identified and structured.Source: §1 · p. 3

Main results

• Data-driven methods outperform zero-shot and few-shot inference, with Literature + Data performing best and Qwen producing the most effective and generalizable real-world hypotheses.On synthetic tasks, the best model discovers 93.8% of ground-truth hypotheses in base cases but only 38.8% as difficulty increases.Source: §1 · p. 3

Benchmark motivation

• HypoBench introduces a systematic benchmark combining 194 datasets across 7 real-world and 5 synthetic domains.The benchmark is designed to clarify hypothesis generation and support robust progress in the area.Source: §1 · pp. 2–3, Figure 1 · p. 2

Benchmark design principles

• The benchmark is designed around realistic scenarios, diverse hypothesis-generation skills, variable difficulty, and accurate evaluation.Tables provide examples of the generation pipeline and hypotheses for both real-world and synthetic datasets.Source: §2 · p. 4, Table 3 · p. 6, Table 4 · p. 6

Real-world and synthetic datasets

• The benchmark contains 194 datasets spanning 12 domains, including 7 real-world classification tasks and 5 synthetic domains.Real-world tasks include deceptive-review, AI-generated-content, persuasive-argument, mental-stress, news-engagement, retweet, and paper-citation prediction.Source: §2 · p. 4
• Real-world tasks use relevant literature plus in-domain and out-of-domain splits to assess hypothesis generalizability, but their unknown ground truth limits precise evaluation.Synthetic datasets therefore provide controlled mechanisms for measuring recovery of true hypotheses.Source: §2 · p. 4

Synthetic task construction

• Synthetic data generation uses logistic regression and decision trees to create interpretable linear, nonlinear, and interaction-based relationships with explicit ground-truth hypotheses.These modeling choices support precise evaluation of discovered relationships.Source: §2 · pp. 4–5
• An abstraction layer converts latent variables into unstructured natural-language observations, requiring models to infer relevant features before discovering their relationships with outcomes.This tests the full pipeline from raw observations to interpretable hypotheses rather than pattern matching over structured features.Source: §2 · p. 5

Difficulty control

• Synthetic difficulty varies through label noise, feature count, decision-tree depth, distractor features, and textual subtlety.These controls test robustness, feature selection, compositional reasoning, and abstraction from natural language.Source: §2 · p. 5

Evaluation principles

• The framework addresses evaluations that overemphasize novelty by prioritizing explanatory power and plausibility.Novelty is treated as a separate consideration rather than the sole criterion of hypothesis quality.Source: §3 · pp. 6, 8

Practical utility

• Practical utility measures whether discovered hypotheses support accurate predictions on held-out test data.Classification accuracy and F1 are computed from predictions guided by discovered features and their relationships.Source: §3 · pp. 6–7

Hypothesis discovery rate

• Hypothesis discovery rate combines feature discovery accuracy with correctness of the recovered relationships on synthetic datasets.HDR is recall-oriented and complements practical utility when ground-truth hypotheses are available.Source: §3 · p. 7

Generalizability

• Generalizability is tested across in-domain and out-of-domain splits, and across different inference models, using accuracy and F1 scores.These evaluations are conducted only on real-world datasets.Source: §3 · p. 7

Qualitative evaluation

• Qualitative evaluation assesses novelty, plausibility, and clarity as three key properties determining hypothesis quality, with GPT-4o judging these qualities using generated hypotheses and relevant context from existing literature.Source: §3 · p. 7

Models

• Four LLMs—GPT-4o-mini, Qwen-2.5-72B-Instruct, Llama-3.1-70B-Instruct, and DeepSeek-R1-Distilled-Llama-70B—are evaluated across inference settings.A finetuned Llama-3.1-8B model provides an additional real-dataset comparison point.Source: §4 · p. 8

Hypothesis generation methods

• The benchmark evaluates hypothesis-generation methods across all metrics, providing a comprehensive comparison of approaches.The methods are tested with state-of-the-art LLMs including GPT, Llama, Qwen, and DeepSeek.Source: §4 · p. 8

Baselines

• Zero-shot inference and zero-shot generation test raw task knowledge with and without explicitly generated hypotheses.Zero-shot inference uses only the task description, whereas zero-shot generation creates hypotheses before classification.Source: §4 · p. 8

Literature-based generation

• Literature-based generation summarizes relevant research before producing hypotheses and is evaluated exclusively on real-world datasets.It adapts a literature-only approach for the benchmark.Source: §4 · p. 8

Iterative methods

• IO Prompting generates hypotheses from labeled examples, while Iterative Refinement revises them using misclassified examples and empirical feedback.The iterative method is reimplemented with the original work’s hyperparameters.Source: §4 · p. 8
• HypoGeniC maintains a reward-scored hypothesis bank, while Literature + Data adds scientific literature to observational-data analysis during iterative refinement.The latter combines empirical evidence and domain knowledge through data-analysis and literature agents.Source: §4 · p. 8

Real-world results

• Data-driven hypothesis-generation methods outperform simple inference approaches, with Literature + Data achieving the best real-world performance.These methods extract and synthesize more useful information from data than few examples alone, while integrating literature and empirical data provides complementary benefits.Source: §5 · pp. 8–9
• Qwen performs best with Literature + Data, exceeding other models by 2.5% average accuracy, whereas Llama leads few-shot inference by 5.3%.Qwen’s hypotheses are described as effective and generalizable, while Llama shows strong in-context learning.Source: §5 · p. 9

Cross-model generalization

• Generated hypotheses generalize across Qwen, Llama, and DeepSeek, with only a 3.4% average accuracy drop versus the original generation model.The strongest transfer occurs between Llama and DeepSeek, while GPT differs substantially from the other models.Source: §5 · pp. 9–10, Table 6 · p. 10

Qualitative evaluation

• Literature-only hypotheses score highest in plausibility but lowest in novelty, while Iterative Refinement achieves the highest novelty.No method excels simultaneously on plausibility and novelty, making their balance a persistent challenge.Source: §5 · p. 10, Table 7 · p. 10

Synthetic datasets

• DeepSeek reaches 93.8% HDR at base synthetic difficulty but falls to 40.0% with outcome noise and 38.3% with distractors.GPT starts at 75.0% HDR and is somewhat less affected by noise and distractors, suggesting a possible trade-off between coverage and robustness.Source: §5 · pp. 10–11, Figure 2 · p. 11
• Feature interactions beyond depth 2 sharply reduce performance, with the best model reaching only 38.8% HDR at depths 3 and 4.Qwen and Llama improve from depth 1 to depth 2, indicating stronger recovery of two-feature interactions, whereas implicit features also reduce performance.Source: §5 · p. 11

Model priors

• Model priors strongly affect synthetic performance: base HDR exceeds 50% for College Admission and Shoe Sales but remains below 40% at harder levels for Presidential Election and Personality Prediction.Counterintuitive settings are especially difficult; under increasing complexity, all models average below 15% HDR, and Qwen reaches 0% under high distractor conditions.Source: §5 · pp. 11–12

Evaluation validation

• Human-human agreement is substantial to almost perfect, while model-human agreement is substantial, with 78% of relationship-correctness predictions within one point of consensus.These results support LLM-based HDR evaluation as a reliable approximation of human judgment.Source: §5 · pp. 12–13, Table 8 · p. 13

Benchmarks for research tasks

• Existing research benchmarks cover agentic data analysis, literature processing, information retrieval, and broader scientific research tasks.These include DiscoveryBench and related benchmarks for scientific discovery and research workflows.Source: §6 · p. 13

Related discovery tasks

• D5 describes distributional differences between text corpora, whereas HypoBench emphasizes explanatory power, practical utility, HDR, and latent-feature abstraction.HypoBench therefore targets explanatory hypotheses rather than comparative corpus descriptions.Source: §6 · p. 13

Research agents

• Unlike ideation-focused systems, HypoBench evaluates hypotheses that explain observed real-world phenomena.Related work includes methods for generating novel research ideas, explaining phenomena, and automating research processes.Source: §6 · p. 13

Evaluation gap

• HypoBench introduces standardized multidimensional evaluation because existing approaches largely rely on human judgment or LLM-as-a-judge.The benchmark aims to support more rigorous and principled development of hypothesis-generation systems.Source: §6 · p. 13

• HypoBench evaluates hypothesis generation across explanatory power, practical utility, and generalizability in real-world and synthetic tasks.Source: §7 · p. 14
• Existing methods provide some explanatory value and outperform few-shot inference, but their performance remains limited.Source: §7 · p. 14
• HypoBench supports future research on more effective hypothesis generation and broader scientific-discovery tasks.Source: §7 · p. 14

8 Limitations

• Qualitative novelty, plausibility, and clarity ratings rely on LLM judges without human validation, whereas HDR shows substantial model–human agreement for FDR and RC.Agreement was κ = 0.71 for FDR and weighted κw = 0.64 for RC; practical utility is emphasized as the primary metric because it directly measures held-out predictive performance.Source: §8 · p. 14
• HDR measures recovery of ground-truth hypotheses as a recall-oriented metric rather than explanatory power, and the benchmark currently focuses on binary classification.Practical utility complements HDR by assessing predictive accuracy, while regression and open-ended scientific discovery remain future extensions.Source: §8 · p. 14
• Synthetic datasets provide controlled ground-truth evaluation rather than direct evidence of real-world discovery performance, making their gap from real tasks diagnostic rather than a transfer claim.Even O3 achieves only 0.52 average HDR on a subset of synthetic datasets (§ C.4), underscoring HypoBench’s difficulty.Source: §8 · p. 14

A.2 Hypothesis Evaluations

• Hypotheses are scored from 0.0 to 1.0 by assessing both variables and relationship directions, with partial credit for composite or partially correct hypotheses.Contradictory, invalid, or empty hypotheses receive 0.0, while irrelevant extra information is ignored when core relationships are correct.Source: §A.2 · p. 21

B.1 Paper Citation

• The paper-citation dataset uses abstract-only inputs and filtered OpenAlex records, yielding 5,324 data points while supporting cross-field out-of-distribution evaluation.The construction excludes incomplete abstracts and non-article content; citation labels use extreme top- and bottom-cohort percentiles, with n = 2 years and α = 0.1 in the reported dataset.Source: §B.1 · pp. 22–23

B.2 Marine Ecosystem

• The marine-ecosystem task contains 14 noise variables alongside a ground-truth sunlight–water-clarity hypothesis, and evaluates regression performance using mean squared error.The dataset is adapted from DiscoveryBench (Majumder et al., 2024), with daily sunlight hours as the prediction target and results reported in Table 11.Source: §B.2 · p. 23

B.3 Dataset Generation for Presidential Election and Personality Prediction

• Synthetic presidential-election and personality datasets combine templates, generated features, and classifier-derived labels to create structured textual inputs.LLMs generate labels, feature types, feature values, and templates; exhaustive substitution captures feature interactions.Source: §B.3 · pp. 23–24
• The dataset defines six predefined difficulty levels, including feature-based label generation, two distractor feature types at Level 2, 10% label noise at Level 3, and 25% random logistic-regression weight dropout at Level 4.Source: §B.3 · p. 24

B.4 Dataset Generation for College Admission and Shoe Sales

• College-admission and shoe-sales tasks use decision trees with adjustable complexity, noise, distractors, and counterintuitive hypotheses to test reasoning under misleading priors.College admission varies four difficulty controls, while shoe sales uses one- or two-level trees.Source: §B.4 · pp. 25–26

B.5 Synthetic Dataset Generation Examples

• Ground-truth hypotheses map each feature to likely or unlikely classes using logistic-regression weight polarity and relative magnitude.Positive, negative, and zero weights correspond to increased likelihood, decreased likelihood, and neutrality.Source: §B.5 · p. 28

C.1 Marine Ecosystem Results

• Iterative Refinement and HypoGeniC achieve the lowest errors for predicting sunlight hours on the marine ecosystem dataset.The reported comparison is based on GPT results summarized with Table 11.Source: §C.1 · p. 28, Table 11 · p. 29
• All four hypothesis-generation methods find the true features, so relationship correctness becomes the sole determinant of hypothesis discovery rate.Feature discovery rate indicates complete true-feature recovery.Source: §C.1 · p. 29
• Hypothesis discovery rate is less accurate than mean squared error for judging generated-hypothesis quality in this task.The authors attribute this limitation to difficulty quantifying relationship correctness manually.Source: §C.1 · p. 29

C.2 IND Results on Real Datasets

• Finetuned Llama outperforms Qwen with Literature + Data by 8.72%, while Qwen otherwise leads average accuracy by 2.49%.Literature + Data is the best hypothesis-generation method on the IND real datasets.Source: §C.2 · p. 29, Table 12 · p. 29
• Qwen, Llama, and DeepSeek effectively use hypotheses from other models, whereas GPT-generated hypotheses transfer poorly in both directions.The comparison concerns cross-model inference on IND data using Literature + Data hypotheses.Source: §C.2 · pp. 29–30, Table 13 · p. 29

C.3 Additional Results on Synthetic Datasets

• Across synthetic configurations, GPT has the best average HDR and accuracy, while HypoGeniC outperforms other generation methods by a large margin.Tables 14 and 15 aggregate HDR, accuracy, and F1 across difficulty configurations.Source: §C.3 · p. 31, Table 14 · p. 31, Table 15 · p. 31
• The best synthetic result reaches only 50.02% average accuracy and 46% average HDR, showing that full ground-truth recovery remains difficult.These aggregate results motivate HypoBench as a resource for improving hypothesis-generation systems.Source: §C.3 · p. 31, Table 14 · p. 31, Table 15 · p. 31

C.4 Evaluation with O3 Reasoning Model

• O3 achieved an average HDR of 0.52 on synthetic datasets, with near-perfect feature discovery (FDR=0.99) but moderate relationship correctness (RC=0.52).Source: §C.4 · p. 31, Table 16 · p. 32
• O3 reaches perfect HDR on simpler tasks but only 0.25 on complex tasks such as admission/level_4 and preference/level5.The evaluation supplies O3 complete training data and requests exactly 20 hypotheses.Source: §C.4 · p. 31
• Compared with HypoGeniC using GPT-4o-mini, O3 wins on admission/level_3 but loses on shoe_simple.The reported task-level comparisons are 0.45 versus 0.30 and 0.75 versus 0.88, respectively.Source: §C.4 · p. 31
• These results show that HypoBench remains challenging even for state-of-the-art reasoning models.The authors present the benchmark as a resource for improving hypothesis-generation capabilities.Source: §C.4 · p. 31

D.1 Implementation Details

• Six methods are implemented through direct prompting, literature use, iterative refinement, HypoGeniC, and Literature + Data configurations.Zero-shot and literature-based generation serve as baselines, while the other methods use original-paper or official-code implementations.Source: §D.1 · p. 32
• Zero-shot generation prompts LLMs with task descriptions and instructions without additional information, exposing their existing knowledge and hypothesis-extraction ability.Source: §D.1 · p. 32
• Literature-based generation follows Liu et al. (2025), using curated data, default hyperparameters, and the method’s generation prompts across experiments.Source: §D.1 · p. 32
• IO Prompting and Iterative Refinement trains for 3 epochs with 10 examples, generates 5 hypotheses per iteration, and updates hypotheses using feedback.Source: §D.1 · p. 32
• HypoGeniC and Literature + Data use hypothesis banks of 20, initialize with 10 examples, and train for one epoch on 200 examples.Literature + Data additionally performs 6 refinement rounds.Source: §D.1 · p. 32

D.2 Costs

• Complete pipeline execution costs approximately $5.5 per real dataset and $2 per synthetic dataset, with 4 hours on 4 NVIDIA A100s.Real-dataset costs include approximately $1.5 for generation and $1 for qualitative ratings; synthetic-dataset costs include approximately $1.2 for generation and $0.2 for HDR evaluations.Source: §D.2 · p. 32