Preemptive Answer "Attacks" on Chain-of-Thought Reasoning

• URL: http://arxiv.org/abs/2405.20902v1
• Date: Fri, 31 May 2024 15:15:04 GMT
• Title: Preemptive Answer "Attacks" on Chain-of-Thought Reasoning
• Authors: Rongwu Xu, Zehan Qi, Wei Xu,
• Abstract summary: Large language models (LLMs) showcase impressive reasoning capabilities when coupled with Chain-of-Thought prompting. In this paper, we introduce a novel scenario termed preemptive answers, where the LLM obtains an answer before engaging in reasoning. Experiments reveal that preemptive answers significantly impair the model's reasoning capability across various CoT methods and a broad spectrum of datasets.
• Score: 7.233752893356647
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) showcase impressive reasoning capabilities when coupled with Chain-of-Thought (CoT) prompting. However, the robustness of this approach warrants further investigation. In this paper, we introduce a novel scenario termed preemptive answers, where the LLM obtains an answer before engaging in reasoning. This situation can arise inadvertently or induced by malicious users by prompt injection attacks. Experiments reveal that preemptive answers significantly impair the model's reasoning capability across various CoT methods and a broad spectrum of datasets. To bolster the robustness of reasoning, we propose two measures aimed at mitigating this issue to some extent.

Related papers

• Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods [59.779795063072655]
  Chain-of-Thought (CoT) prompting and its variants have gained popularity as effective methods for solving multi-step reasoning problems. We analyze CoT prompting from a statistical estimation perspective, providing a comprehensive characterization of its sample complexity.
  arXiv  Detail & Related papers  (2024-08-25T04:07:18Z)
• Rethinking harmless refusals when fine-tuning foundation models [0.8571111167616167]
  We investigate the degree to which fine-tuning in Large Language Models (LLMs) effectively mitigates versus merely conceals undesirable behavior. We identify a pervasive phenomenon we term emphreason-based deception, where models either stop producing reasoning traces or produce seemingly ethical reasoning traces that belie the unethical nature of their final outputs.
  arXiv  Detail & Related papers  (2024-06-27T22:08:22Z)
• Chain-of-Probe: Examing the Necessity and Accuracy of CoT Step-by-Step [81.50681925980135]
  We propose a method to probe changes in the mind during the model's reasoning. By analyzing patterns in mind change, we examine the correctness of the model's reasoning. Our validation reveals that many responses, although correct in their final answer, contain errors in their reasoning process.
  arXiv  Detail & Related papers  (2024-06-23T15:50:22Z)
• Towards Faithful Chain-of-Thought: Large Language Models are Bridging Reasoners [19.40385041079461]
  Large language models (LLMs) suffer from serious unfaithful chain-of-thought (CoT) issues. We first study the CoT faithfulness issue at the granularity of CoT steps, identify two reasoning paradigms. We then conduct a joint analysis of the causal relevance among the context, CoT, and answer during reasoning.
  arXiv  Detail & Related papers  (2024-05-29T09:17:46Z)
• Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models [84.15513004135576]
  Current research enhances the reasoning performance of Large Language Models (LLMs) by sampling multiple reasoning chains and ensembling based on the answer frequency. This approach fails in scenarios where the correct answers are in the minority. We introduce a hierarchical reasoning aggregation framework AoR, which selects answers based on the evaluation of reasoning chains.
  arXiv  Detail & Related papers  (2024-05-21T17:12:19Z)
• Distilling Reasoning Ability from Large Language Models with Adaptive Thinking [54.047761094420174]
  Chain of thought finetuning (cot-finetuning) aims to endow small language models (SLM) with reasoning ability to improve their performance towards specific tasks. Most existing cot-finetuning methods adopt a pre-thinking mechanism, allowing the SLM to generate a rationale before providing an answer. This mechanism enables SLM to analyze and think about complex questions, but it also makes answer correctness highly sensitive to minor errors in rationale. We propose a robust post-thinking mechanism to generate answers before rationale.
  arXiv  Detail & Related papers  (2024-04-14T07:19:27Z)
• Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering [59.495717939664246]
  Large language models have manifested remarkable capabilities by leveraging chain-of-thought (CoT) reasoning techniques to solve intricate questions. We propose a novel approach called the selective filtering reasoner (SelF-Reasoner) that assesses the entailment relationship between the question and the candidate reasoning chain. SelF-Reasoner improves the fine-tuned T5 baseline consistently over the ScienceQA, ECQA, and LastLetter tasks.
  arXiv  Detail & Related papers  (2024-03-28T06:28:35Z)
• Navigating the OverKill in Large Language Models [84.62340510027042]
  We investigate the factors for overkill by exploring how models handle and determine the safety of queries. Our findings reveal the presence of shortcuts within models, leading to an over-attention of harmful words like 'kill' and prompts emphasizing safety will exacerbate overkill. We introduce Self-Contrastive Decoding (Self-CD), a training-free and model-agnostic strategy, to alleviate this phenomenon.
  arXiv  Detail & Related papers  (2024-01-31T07:26:47Z)
• Question Decomposition Improves the Faithfulness of Model-Generated Reasoning [23.34325378824462]
  Large language models (LLMs) are difficult to verify the correctness and safety of their behavior. One approach is to prompt LLMs to externalize their reasoning, by having them generate step-by-step reasoning as they answer a question. This approach relies on the stated reasoning faithfully reflecting the model's actual reasoning, which is not always the case. Decomposition-based methods achieve strong performance on question-answering tasks, sometimes approaching that of CoT.
  arXiv  Detail & Related papers  (2023-07-17T00:54:10Z)
• Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting [43.458726163197824]
  Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output. We find that CoT explanations can systematically misrepresent the true reason for a model's prediction.
  arXiv  Detail & Related papers  (2023-05-07T22:44:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.