Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives (2024)

Abstract: The reflection capacity of Large Language Model (LLM) has garnered extensive attention. A post-hoc prompting strategy, e.g., reflexion and self-refine, refines LLM’s response based on self-evaluated or external feedback. However, recent research indicates without external feedback, LLM’s intrinsic reflection is unstable. Our investigation unveils that the key bottleneck is the quality of the self-evaluated feedback. We find LLMs often exhibit overconfidence or high randomness when self-evaluate, offering stubborn or inconsistent feedback, which causes poor reflection. To remedy this, we advocate Self-Contrast: It adaptively explores diverse solving perspectives tailored to the request, contrasts the differences, and summarizes these discrepancies into a checklist which could be used to re-examine and eliminate discrepancies. Our method endows LLM with diverse perspectives to alleviate stubborn biases. Moreover, their discrepancies indicate potential errors or inherent uncertainties that LLM often overlooks. Reflecting upon these can catalyze more accurate and stable reflection. Experiments conducted on a series of reasoning and translation tasks with different LLMs serve to underscore the effectiveness and generality of our strategy.

## introduction mastering reasoning and decision-making capabilities is of utmost importance to paving the way for artificial general intelligence. recently, large language models (llms) @xcite @xcite @xcite @xcite and applications built on them @xcite @xcite demonstrate impressive capabilities in various domains, especially combined with chain-of-thought (wei et al., 2022; † corresponding author. ## evaluation of intrinsic reflection we first comprehensively investigate the intrinsic reflection capability of llms, i.e., llms selfevaluate the initial response without external feedback and then refine it. subsequently, we methodically investigate the factors influencing reflection. ## self-contrast prior sections illustrate the challenges llms encounter in accurately evaluating previous solutions, often resulting in overconfident or inconsistent feedback. concurrently, we observe that leveraging the discrepancies between two different solutions can inspire a more efficacious reflection, notably reducing the uncertainty during the reflection. building upon this insight, we propose a more diverse inter-perspective self-contrast, facilitating more reliable self-reflection. self-contrast consists of three procedures: create diverse perspectives, contrast inter-perspective discrepancies, and eliminate discrepancies. in create diverse perspectives ( § 3.1), we encourage llms to autonomously create a variety of prompts tailored to the user's request, each offering a unique perspective for problem-solving, e.g., different thinking styles, diverse identities, personalities, or preferences. these diverse perspectives prompt the llm to generate different responses. in the second stage ( § 3.2), llm contrasts the differences between each pair of responses. lastly ( § 3.3), to eliminate discrepancies, we abstract these differences into a detailed checklist for re-examining. this checklist guides the llm to meticulously examine the causes of discrepancies, including random errors or intrinsic biases, which result in inconsistent results among perspectives. as shown in figure 2 , llm designs five different prompts and their translation results based on the user's request ("这个计划被枪毙") . from a literal perspective, the phrase "被枪毙" is translated as "shot to death". this rigid translation fails to grasp the metaphor embedded in the military term. conversely, from a liberal perspective, it is translated as "this plan was axed". after contrasting two different translations, llms believe they should scrutinize the source sentence for metaphors and ensure the translation aligns with the conventions of english expression. ## the effect of the different components the above results show that self-contrast inspires reflection more accurately and stably than direct evaluation. it encompasses a self-curated prompt process, which fosters diverse solving perspectives to mitigate self-evaluation biases. besides, it involves a checklist generation process to facilitate re-examination. we analyze their effect as follows: self-curated prompt vs. sampling multiple responses. instead of self-curated prompt process, we directly sample multiple responses from llms for subsequent contrast and reflection. figure a2 shows that the final accuracy improves as the number of sampled responses increases, yet it is still lower than self-contrast with self-curated prompts ## discussions self-contrast switches the critique objective into a contrastive task. we transform the selfevaluation into a process of comparing differences, explicitly altering the attention distribution of the llm. the llm is required only to identify the differences between two solutions, without judging right or wrong. this process is less influenced by the biases inherent in llms, as the objective is contrasting rather than evaluation. besides, in contrasting results can help llms notice overlooked details and biases. after contrasting the differences between the two solutions, we summarize these differences into a checklist, thereby explicitly prompting the llm to focus on the logical pitfalls and other issues underlying these differences. this allows llms to engage in reflection more clearly and purposefully. as shown in figure 2 , llm generates different translations for the user's request: "this plan was shot to death", and "this plan was axed". the former is a rigid translation that fails to grasp the metaphor embedded in the military term. after contrasting two different translations, llms believe they should scrutinize the source sentence for metaphors and ensure the translation aligns with the conventions of english expression. ## conclusion we conduct a comprehensive investigation into the inherent reflection capabilities of llms. our find-ings reveal a notable challenge: in the absence of external feedback, llms struggle to correct errors in previous responses on their own. after analyzing their self-evaluate process, we discover that llms are unable to accurately evaluate prior solutions and often provide overconfident or inconsistent feedback, which impedes reflection. to mitigate this, we introduce self-contrast, a contrastive strategy that inspires reflection by contrasting the differences between multiple perspectives, providing an informative checklist for reflection. our experiments show that self-contrast performs well across a variety of scenarios and with different llms.