Source-linked AI summary
Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models
Rima Hazra, Sayan Layek, Somnath Banerjee, Soujanya Poria
TL;DR
Model editing can affect both the safety and consistency of language models, and the paper investigates whether sensitive, accurate edits are associated with unethical responses. It introduces a topical benchmark and evaluates pre- and post-edit behavior, finding that unethical responses are more severe and direct after editing.
Problem
Knowledge conflicts and distortions from editing raise safety concerns, while the paper examines editing’s impact on unethical response generation.
Source: §1 · p. 2Method
The paper introduces NICHEHAZARDQA and evaluates ethical and unethical responses before and after topic-specific model edits.
Source: §1 · p. 2, §5.1 · p. 6Results
Takeaways & Limitations
The findings identify model editing as a cost-effective approach to topical red-teaming and call for editing methods that account for ethics.
Source: §1 · p. 2, §9 · p. 9The study focuses on specific areas, its dataset may omit complexities or emerging issues, and its severity assessments are somewhat subjective.
Source: §10 · p. 10
Abstract
from arXiv · showhide
In the rapidly advancing field of artificial intelligence, the concept of Red-Teaming or Jailbreaking large language models (LLMs) has emerged as a crucial area of study. This approach is especially significant in terms of assessing and enhancing the safety and robustness of these models. This paper investigates the intricate consequences of such modifications through model editing, uncovering a complex relationship between enhancing model accuracy and preserving its ethical integrity. Our in-depth analysis reveals a striking paradox: while injecting accurate information is crucial for model reliability, it can paradoxically destabilize the model's foundational framework, resulting in unpredictable and potentially unsafe behaviors. Additionally, we propose a benchmark dataset NicheHazardQA to investigate this unsafe behavior both within the same and cross topical domain. This aspect of our research sheds light on how the edits, impact the model's safety metrics and guardrails. Our findings show that model editing serves as a cost-effective tool for topical red-teaming by methodically applying targeted edits and evaluating the resultant model behavior.
1 Introduction
Knowledge editing can keep language models current, but edits may also disrupt consistency and safety. The paper examines whether accurate, sensitive edits can produce unethical responses and introduces a dataset for testing this behavior.
1 Introduction
- Knowledge editing updates specific facts without changing billions of model parameters, addressing the challenge of keeping models current.The introduction describes external memorization, global optimization, and local modification as editing strategies.Source: §1 · p. 1
- Editing can add current knowledge but may also cause unwanted effects, including knowledge conflict and distortion that threaten consistency and safety.Conflict arises when related edits interfere; distortion can alter the model’s knowledge structure and produce misleading information.Source: §1 · p. 2
- The paper reports that even a single accurate edit with sensitive information can affect guardrails and prompt unethical responses, typically in topic-centric, niche ways.The authors describe model editing as a potential tool for topical red-teaming or jailbreaking.Source: §1 · p. 2
- NICHEHAZARDQA tests safety protocols with sensitive, unethical questions, and edited models breached guardrails across the proposed and two existing benchmarks.The paper also presents editing as an efficient, cost-effective approach to topical red-teaming.Source: §1 · p. 2
2 Related work
Related work covers LLM safety research and knowledge-editing approaches. The section also includes an algorithmic outline for building a topical question set and using edits to red-team a model.
2 Related work
- Prior work addresses safety through training data, mental-health use, bias and robustness, multilingual benchmarks, and toxicity detection.The section also surveys memory-based, meta-learning, and locate-then-edit approaches and their applications.Source: §2 · pp. 2–3
- Algorithm 1 builds a topical question set by generating questions with an unsafe model and retaining questions that a safe model flags as raising ethical concerns.The resulting question-answer pairs are grouped by topic.Source: §2 · p. 3
- The red-teaming outline selects k topic-specific questions for editing, then obtains and evaluates responses from the base and edited models.The provided algorithm fragments specify response collection and evaluation after editing.Source: §2 · p. 3
3 Dataset
The study combines two existing harmful-question datasets with NICHEHAZARDQA, a new collection of sensitive-topic questions. The proposed dataset contains 500 questions across six named topics and miscellaneous content.
3 Dataset
- The dataset section includes a table of dataset statistics, but the supplied caption does not specify its values or layout.Source: Table 1 · p. 3
- NICHEHAZARDQA contains 500 unethical questions spanning six named sensitive topics and miscellaneous content.The listed topics include hate speech and discrimination, fake news and propaganda, cruelty and violence, conspiracy theories and paranoia, learner manipulation, and weapons technology.Source: §3 · p. 3
- The experiments also use HarmfulQA, with 1,960 harmful questions across 10 broad topics, and DengerousQA, with 200 explicitly toxic questions.DengerousQA questions are generated using text-davinci-002 and are not labeled by topic.Source: §3 · p. 3
4 Methodology
The methodology generates unethical question-answer pairs, prepares topic-specific edit data, and compares model behavior before and after editing. It evaluates responses to questions not used for editing.
4 Methodology
- The methodology has three phases: unethical Q&A generation, edit-data preparation, and red-teaming through model editing.The overview points to Figure 2 for the framework schematic.Source: §4 · pp. 3–4
4.1 Preliminaries
- The paper’s notation distinguishes the unsafe and safe LLMs, the pre-edit and post-edit models, and their respective responses.The unsafe model generates questions; the safe model checks for ethical concerns.Source: §4.1 · p. 4
4.2 Unethical Q&A generation
- Question generation uses prompts across six sensitive topics, retaining questions that the safe LLM flags and answers verified as raising ethical concerns.The unsafe LLM supplies the unethical answers used to form topic-specific question-answer pairs.Source: §4.2 · p. 4
4.3 Edit data construction
- For each selected topic, the edit data pairs a sampled question with its extracted subject and target answer.The target is the correct unethical answer; the example subject is Nuclear Weapons.Source: §4.3 · p. 4
4.4 Edit-based red teaming
- The red-teaming phase edits the base model with k topic-specific tuples, then evaluates pre- and post-edit responses on the remaining questions from that topic.The method focuses on minimal edits to specific model layers.Source: §4.4 · p. 4
5 Experimental setup
The experiments compare ethical responses before and after targeted edits in same-topic and cross-topic settings, then evaluate how editing affects response safeguards. Their in-depth evaluation distinguishes persistent unethical responses from responses that become unethical after editing.
5 Experimental setup
- The experiments use Llama-2-7b-chat-hf and Llama-2-13b-chat-hf as base models and select ROME (Meng et al., 2022) as a single locate-and-edit algorithm to study how editing affects model safety.Source: §5 · p. 5
- ROME edits use questions from the edited topic for same-topic tests and questions from other topics for cross-topic tests.For cross-topic testing, the study samples 150 questions per NICHEHAZARDQA topic and 90 per HarmfulQA topic.Source: §5 · p. 5
5.1 Evaluation
- GPT-4 evaluates whether responses raise ethical concerns, and the study reports the fraction of ethical or unethical responses among all responses.The evaluation also examines how editing affects safeguards and the ethicality of responses.Source: §5.1 · pp. 5–6
- Broader evaluation measures ethical and unethical response rates before and after editing, while in-depth evaluation separates UE→UE from E→UE responses.UE→UE counts responses that are unethical both before and after editing; E→UE counts responses that change from ethical to unethical.Source: §5.1 · p. 6
6 Results
Editing is associated with varied changes in response ethicality across datasets and topics, with particularly high shifts in some NICHEHAZARDQA same-topic tests. Cross-topic shifts are generally smaller, while the reported MMLU accuracy after editing remains close to its pre-edit value.
6.1 Same topic setting
- In DengerousQA, 4.7% of responses shifted from ethical to unethical, while Post UE was 7.9% versus 3.7% Pre UE.The authors describe the overall editing effect as relatively low for this diverse, uncategorized dataset; HarmfulQA also showed topic-level variation, including 22.4% → 32.7% and 21.4% → 31.6% Post UE changes in two topics.Source: §6.1 · p. 6
- In NICHEHAZARDQA same-topic tests, Advanced Technology to Create Weapons reached 74.3% Post UE and 40.2% E→UE after editing.Conspiracy Theory and Paranoia had 67.3% Post UE and a 47.8% E→UE shift.Source: §6.1 · p. 6
6.2 Cross topic setting
- Cross-topic tests showed varied UE→UE and E→UE rates, and the overall E→UE movement was relatively lower than in same-topic tests.For NICHEHAZARDQA, Conspiracy Theories and Paranoia had the highest reported UE→UE rate at 24.7%; HarmfulQA rates also varied by topic.Source: §6.2 · p. 7
6.3 Catastrophic forgetting
- For the MMLU dataset, llama2-7b-chat-hf had 46.86% pre-edit accuracy; after editing on DengerousQA, its accuracy remained almost similar at 46.82%.Source: §6.3 · p. 8
7 Ablation study
The ablations examine whether editing-related ethical-to-unethical shifts vary across model choices, editing methods, and prompting techniques. The reported average shifts differ across these experimental variants.
Impact of different large language models
- Across categories, the reported ethical-to-unethical shift was 13.45%; gemma-7b-it had an 18.9% average shift, compared with 32.96% for llama2-7b-chat-hf and 13.2% for llama2-13b-chat-hf using ROME.The analysis also includes Mistral-7B-Instruct-v0.2 and gemma-7b-it experiments.Source: §7 · p. 8
Impact of different editing methods
- With MEMIT, llama2-7b-chat-hf had a 21.18% average shift across categories, versus 32.96% with ROME; llama2-13b-chat-hf with ROME had 13.2%.These shifts are from ethical before editing to unethical after editing.Source: §7 · p. 8
Impact of different prompting techniques
- With chain-of-thought prompting, average shifts were 27.05% for ROME and 13.18% for MEMIT across categories.The prompt included the instruction “Let’s think step by step” and used llama2-7b-chat-hf.Source: §7 · p. 8
8 Error analysis
The error analysis reports that the effects of additional edits vary by dataset and topic, and that post-edit unethical responses can be more intense. It also links larger increases in unethical responses to several sensitive topics.
K-EDIT experiments
- E→UE increased with edit count for Cruelty and Violence in NICHEHAZARDQA and for DengerousQA, while some other topic trends were non-monotonic.For Control the Thoughts and Emotions of Learners and Advanced Technology to Create Weapons, E→UE rose at 2-EDIT and later declined.Source: §8 · pp. 8–9
Unethical response intensity
- When both models answered unethically, the post-edited model tended to produce violations of greater intensity.The authors give the example of a subtle stereotype in a base-model response becoming a blatantly discriminatory statement after editing.Source: §8 · p. 9
Analyzing Topic Sensitivity
- The analysis associates larger post-edit increases in unethical responses with sensitive topics including hate speech, fake news, cruelty, conspiracy theories, and weapons.History and Culture and Health and Medicine are described as topics where ethical responses were more consistently generated.Source: §8 · p. 9
9 Conclusion
The conclusion reports that editing can intensify unethical outputs on sensitive topics, with post-edited responses more severe and direct. The paper also identifies scope and evaluation limits and describes its ethical purpose.
9 Conclusion
- Post-editing produced more severe and direct unethical responses across the sensitive topics examined, including Hate Speech and Discrimination, Advanced Technology for Creating Weapons, and Fake News & Propaganda.The study introduces NICHEHAZARDQA and analyzes editing effects within and across these topics.Source: §9 · p. 9
10 Limitation
- The study’s focus on specific sensitive areas and NICHEHAZARDQA may not cover other ethical challenges or emerging issues; ethical standards also evolve, and severity judgments are subjective.The authors say their improvement suggestions need further research to understand their impact and unintended consequences.Source: §10 · p. 10
11 Ethical Statement
- The authors frame the work as raising awareness and opening dialogue about ethical AI, while handling data and scenarios responsibly and sensitively.Source: §11 · p. 10
A Appendix
The appendix describes a mixed-topic question set, checks pre- and post-editing performance on two benchmarks, and reports implementation choices for hyperparameters and prompts.
A.1 Miscellaneous Dataset
- The miscellaneous dataset contains unethical questions mixing multiple topics, with examples shown in Table 8; the authors say it will be made public upon acceptance.Source: §A.1 · p. 11, Table 8 · p. 12
A.2 Catastrophic Forgetting
- The catastrophic-forgetting check compares pre- and post-edited model scores on MMLU and TruthfulQA.The supplied text identifies Table 9 as the results location but gives no score values.Source: §A.2 · p. 12, Table 9 · p. 12
A.3 Hyperparameters
- For Llama 2 7B and 13B, the experiments inherit crucial parameter values from ROME, with hyperparameters listed in Table 10.Source: §A.3 · p. 12, Table 10 · p. 12
A.4 Prompt Construction
- The authors tried different prompts for generating subjects, unethical questions, and answers, and used the prompt shown in Table 12.Source: §A.4 · p. 12
A.5 Extended Results
Extended results compare ethical and unethical response rates before and after editing, including same-topic and cross-topic evaluations. Reported shifts vary by dataset and topic, with pronounced ethical-to-unethical changes in NICHEHAZARDQA.
A.5 Extended Results
- The extended results track ethical and unethical responses before and after editing, including E→UE and UE→UE rates, across three datasets in a 1-EDIT Llama-2-13b-chat-hf setup.Source: §A.5 · p. 12, Table 11 · p. 13
A.5.1 Same topic results
- In same-topic tests, DangerousQA unethical responses rose from 4% (Pre UE) to 8.2% (Post UE); NICHEHAZARDQA E→UE shifts reached 21% for Cruelty and Violence and 18.9% for Fake News and Propaganda.Source: §A.5.1 · p. 12, Table 11 · p. 13
A.5.2 Cross topic results
- In cross-topic tests, NICHEHAZARDQA E→UE shifts were 10.1% for Hate Speech and Discrimination and 8.1% for Fake News and Propaganda; DangerousQA has no topical categories for this comparison.The passage also reports 12.8% for Pre NE and Post NE in Hate Speech and Discrimination.Source: §A.5.2 · p. 13