Unveiling Confirmation Bias in Chain-of-Thought Reasoning (2025)

Abstract: Chain-of-thought (CoT) prompting has been widely adopted to enhance the reasoning capabilities of large language models (LLMs). However, the effectiveness of CoT reasoning is inconsistent across tasks with different reasoning types. This work presents a novel perspective to understand CoT behavior through the lens of confirmation bias in cognitive psychology. Specifically, we examine how model internal beliefs, approximated by direct question-answering probabilities, affect both reasoning generation ( Q \to R ) and reasoning-guided answer prediction ( QR \to A ) in CoT. By decomposing CoT into a two-stage process, we conduct a thorough correlation analysis in model beliefs, rationale attributes, and stage-wise performance. Our results provide strong evidence of confirmation bias in LLMs, such that model beliefs not only skew the reasoning process but also influence how rationales are utilized for answer prediction. Furthermore, the interplay between task vulnerability to confirmation bias and the strength of beliefs also provides explanations for CoT effectiveness across reasoning tasks and models. Overall, this study provides a valuable insight for the needs of better prompting strategies that mitigate confirmation bias to enhance reasoning performance. Code is available at https://github.com/yuewan2/biasedcot .

## introduction chain-of-thought (cot) prompting @xcite , which explicitly guides the models to generate intermediate reasoning steps, is one of the most acknowledged prompting strategies for enhancing the reasoning capability of large language models (llms). aside from its benefits of revealing the thinking process in a human-readable format @xcite , it has proven to be significantly effective in complex reasoning tasks @xcite @xcite . to investigate the key factors behind the effectiveness of cot reasoning, prior studies have figure 1 : a typical venn diagram of confirmation bias in cognitive psychology, using the example of a commonsensical question. the agent reinforces its internal beliefs and skews its reasoning process towards "making music", while overlooking other relevant facts of playing guitar. notes that the internal belief is unobserved but plays a huge role in decision making. in this work, we offer a novel perspective from cognitive psychology to understand the cot behaviors across reasoning tasks. we argue that, like human beings, llms can demonstrate the same patterns of confirmation bias @xcite that affects the reasoning process. confirmation bias (figure 1 ) refers to the tendency to selectively retrieve and interpret information in the manner that reinforces preexisting beliefs @xcite . it is often more pervasive in tasks that require subjective interpretation and prior knowledge compared to those involving formal logic and objective correctness @xcite . from this perspective, we seek to answer two questions: 1. how does confirmation bias affect cot behavior? and 2. why does its influence vary across questions, reasoning types, and llms? we begin by approximating internal beliefs using the direct question-answering probabilities, and the answer confidence as an indicator of beliefs strength. to enable a fine-grained analysis, we decompose cot reasoning into two stages of reasoning generation (@xmath0. we then perform correlation analysis between beliefs, rationale attributes, and stage-wise performance to explore patterns of confirmation bias across reasoning tasks and llms. notably, our experiments reveal patterns of confirmation bias in cot. the strength of internal beliefs is found to significantly influence cot performance at both reasoning stages through variations in rationale presentation and how rationale is utilized for answer prediction. the extent of cot improvement also aligns well with the degree to which reasoning tasks are prone to confirmation bias. in addition, we find that "debiasing" internal beliefs becomes even more challenging when they are stronger. this provides a different view of why cot prompting is most effective in symbolic reasoning tasks (e.g., mathematical reasoning) compared to non-symbolic reasoning tasks, which rely more on contextual and implicit knowledge rather than formal rules for problem-solving. it also sheds light on when cot can be more reliably trusted. in summary, we offer a novel perspective from cognitive psychology in undersanding cot behavior, showing that patterns of confirmation bias can influence cot performance across questions, reasoning types, and llms. we also propose a new framework for analyzing cot behavior, which includes the decomposition of the end-to-end accuracy into the performance of @xmath1, along with a stratified correlation analysis that connects model internal beliefs with rationale attributes and stage-wise cot performance. ## preliminary chain-of-thought in the conventional chain-ofthought (cot) @xcite formulation, a reasoning chain r is explicitly decomposed into intermediate steps [r 1 , r 2 , . . . , r t ] given a question q, leading to the final prediction a. in convention, each sentence is treated as a reasoning step. notably, we can factorize cot into a two-stage process as, ## evaluation methods several challenges exist for exploring confirmation bias in cot reasoning of llms. firstly, beliefs b are often internal and unobserved. for llms, the beliefs associated with a question may come from the prior exposure to question-related content during training, making them hard to measure. second, end-to-end accuracy alone is insufficient for analyzing the effects of b at different stages. a finegrained correlation analysis requires a stage-wise performance measure, as well as the quantification of r's attributes given b. third, since we hypothesize that b is a strong prior factor that influences all aspects, it is crucial to develop a method to control its effects in certain analysis. we address each of these challenges in the following sections. we primarily focus on multiple-choice qa questions in this work. ## results ## conclusion in this work, we provide a novel perspective on cot behavior through the lens of confirmation bias from cognitive psychology. we demonstrate that confirmation bias is pervasive in llms, and can substantially impact both reasoning generation and reasoning-guided predictions in the cot process. in addition, we show that confirmation bias can help explain performance variance across different models and datasets. however, our findings also demonstrate the challenges of "debiasing" confirmation bias, particularly when model beliefs are confidently wrong, underscoring the need for further research.