Self-Contradictory Reasoning Evaluation and Detection (2311.09603v4)

Abstract: In a plethora of recent work, LLMs demonstrated impressive reasoning ability, but many proposed downstream reasoning tasks only focus on final answers. Two fundamental questions persist: 1) how consistent is the reasoning, and 2) can models detect unreliable reasoning? In this paper, we investigate self-contradictory (Self-Contra) reasoning, where the model reasoning does not support its answers. To answer 1), we define and assess the Self-Contra rate across three datasets and delve into finer-grained categories of Self-Contra reasoning. We find that LLMs often contradict themselves in reasoning tasks involving contextual information understanding or commonsense. The model may generate correct answers by taking shortcuts in reasoning or overlooking contextual evidence, leading to compromised reasoning. For 2), we task the state-of-the-art model GPT-4 with identifying Self-Contra reasoning and finer-grained fallacies. We find that finer-grained categories enhanced detection can improve GPT-4's ability to detect Self-Contra. However, it is only able to detect Self-Contra with a 52.2% F1 score, much lower compared to 66.7% for humans. Our results indicate that current LLMs lack the robustness necessary for reliable reasoning and we emphasize the urgent need for establishing best practices in comprehensive reasoning evaluations beyond pure performance-based metrics.

• The paper introduces Self-Contra reasoning by categorizing three distinct types where logical inconsistencies occur between reasoning and predictions.
• The study evaluates LLMs using four datasets and five prompting strategies, revealing that high accuracy may mask underlying reasoning flaws.
• The authors propose automatic detection methods and stress the importance of advanced evaluation metrics to ensure reliable reasoning in AI systems.

Overview of "Self-Contradictory Reasoning Evaluation and Detection"

The paper "Self-Contradictory Reasoning Evaluation and Detection" provides a critical analysis of reasoning quality in LLMs with a specific focus on self-contradictory (Self-Contra) reasoning. The authors question the reliability of reasoning in LLMs, especially when models produce seemingly correct answers without sound reasoning. This analysis stems from observing that high accuracy in LLM predictions does not equate to robust, reliable reasoning. The authors, therefore, embark on a systematic evaluation and detection of Self-Contra reasoning to propose improvements in reasoning assessments for LLMs.

Key Insights and Novel Definitions

The authors introduce the concept of Self-Contradictory reasoning, defining three distinct categories: Type1, where correct reasoning leads to an incorrect prediction; Type2, where incorrect reasoning results in a correct prediction; and Type3, where reasoning is inherently self-contradictory. This categorization unveils a critical disjunction between prediction accuracy and reasoning fidelity in LLMs, particularly when models leverage spurious correlations or shortcuts to arrive at answers.

Experimental Evaluation

The paper conducts an evaluative paper across four datasets—WinoBias, WinoGrande, HotPotQA, and CommonSenseQA—applying five distinct prompting strategies, including zero-shot and few-shot promptings. Results from these evaluations reveal that a higher model accuracy often conceals underlying Self-Contra tendencies. The datasets encompass challenges such as social biases and commonsense reasoning, which amplify Self-Contra behaviors in models, especially during zero-shot evaluations.

Analysis of Self-Contra Categories

To gain a comprehensive understanding of Self-Contra reasoning, the authors explore finer-grained categories that describe specific logical fallacies or reasoning errors. They identify issues such as "evidence missing" and "incomplete reasoning" under correct reasoning scenarios, while "questionable cause" and "begging the question" emerge as prevalent issues under incorrect reasoning contexts. The results indicate that, even with improved prompting methods, models struggle to maintain a logical consistency between reasoning and predictions.

Automatic Detection and Evaluation

The paper advances Self-Contra evaluation by proposing automatic detection methodologies, including binary classification and finer-grained fault detection using GPT-4. Although state-of-the-art, GPT-4 did not match human performance in identifying Self-Contra reasoning. This points to a significant challenge in model capability, as models fail to reliably detect their own logical inconsistencies, highlighting a gap between human and artificial cognition in understanding nuanced and self-contradictory reasoning.

Implications and Future Directions

This paper provides actionable insights into the robustness of LLM reasoning processes, emphasizing the importance of evaluation metrics beyond traditional accuracy. The authors propose a new task of Self-Contra reasoning detection, urging the research community to explore the interpretability and trustworthiness of reasoning in AI systems. Future research could investigate effective fine-tuning strategies or architecture modifications aimed at minimizing or mitigating self-contradictory reasoning patterns.

In conclusion, the paper cautions against over-reliance on LLMs for reasoning tasks, urging researchers to address reasoning fidelity as a cornerstone for building reliable, trustworthy LLMs. This work sets the stage for future explorations that not only improve model performance metrics but also ensure that these models engage in credible and coherent reasoning practices.