A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models (2025)

Abstract: Chain-of-thought (CoT) reasoning enhances performance of large language models, but questions remain about whether these reasoning traces faithfully reflect the internal processes of the model. We present the first comprehensive study of CoT faithfulness in large vision-language models (LVLMs), investigating how both text-based and previously unexplored image-based biases affect reasoning and bias articulation. Our work introduces a novel, fine-grained evaluation pipeline for categorizing bias articulation patterns, enabling significantly more precise analysis of CoT reasoning than previous methods. This framework reveals critical distinctions in how models process and respond to different types of biases, providing new insights into LVLM CoT faithfulness. Our findings reveal that subtle image-based biases are rarely articulated compared to explicit text-based ones, even in models specialized for reasoning. Additionally, many models exhibit a previously unidentified phenomenon we term “inconsistent” reasoning - correctly reasoning before abruptly changing answers, serving as a potential canary for detecting biased reasoning from unfaithful CoTs. We then apply the same evaluation pipeline to revisit CoT faithfulness in LLMs across various levels of implicit cues. Our findings reveal that current language-only reasoning models continue to struggle with articulating cues that are not overtly stated.

## introduction large language models (llms) and their multimodal variants have shown exceptional performance on a wide variety of linguistic and visual tasks, and chain-of-thought (cot) reasoning @xcite has emerged as the dominant paradigm for unlocking the reasoning capabilities of these models. typically, a model is prompted to "think step by step" and outline its reasoning before giving the final answer. optionally, the models may be figure 1 : a summary of our results on accuracy gaps vs bias articulation rates, with each point representing a specific model and bias. rl-trained reasoning models are in reddish colors, sft-trained reasoning models are in green colors, and the rest are in blue, gray or brown. rl-trained models (highlighted in orange) have significantly higher bias articulation rates (highlighted in green). an enlarged version is shown in figure 9 trained via sft on curated datasets containing instances of cot reasoning. recently, @xcite and qwen @xcite introduced a new paradigm in which they trained llms via rl on verifiable rewards and produced reasoning llms comparable to openai's o1, which is suspected to use similar methods. these methods have also been applied to lvlms to produce models like qvq @xcite , and supposedly the o3 and o4 series models from openai, claude 3.7 thinking from anthropic, and the gemini 2.5 series models from google. while these methods were developed to imbibe llms with strong reasoning capabilities, they also offer opportunities for studying interpretability from a different angle. by making models produce a cot, we potentially make the inner workings of the model explicitly available in the cot itself, thus getting some interpretability "for free". however, many works @xcite performed careful causal intervention experiments and argue that the chain-of-thought often does not faithfully reflect the true causal factors responsible for the model's output. recently, however, @xcite showed that rltrained reasoning models may be more faithful than non-rl-trained models, and attribute this to the verifiable reward which incentivizes true and faithful cots. this provides hope that interpretability may be much easier for rl-trained models which externalize their reasoning in the cot. however, their experiments were limited to very explicit, textbased biases such as inserting hints in the question indicating that a particular answer was correct. in our paper, we present the first comprehensive study of chain-of-thought faithfulness in large vision-language models (lvlms), addressing a critical gap in current research which has focused exclusively on text-only models. our methodology introduces an evaluation framework that systematically separates bias induction into the model from bias evaluation, enabling more precise analysis of how models incorporate biasing cues into their reasoning processes. this enables us to comment on bias and faithfulness when the model is not being intentionally biased, making it more relevant to practical settings. we evaluate a diverse range of biases across both modalities, including format-based biases (e.g., ordering, position) and content-level biases (e.g., spurious correlations in images, explicit text hints) on a comprehensive selection of instruction-tuned, sft-trained, and rl-trained reasoning models. our findings reveal significant differences in bias articulation patterns across models and training paradigms. similar to @xcite , we observe that rl-trained reasoning models demonstrate substantially higher bias articulation rates compared to instruction-tuned or sft-trained counterparts. importantly, we discover that visual biases are consistently less likely to be articulated than text-based biases, and subtle biases receive considerably less attention in model reasoning traces than explicit ones. experiments on real-world datasets like celeba and waterbirds further validate these observations in practical contexts. we hypothesize that this difference is due to the apparent reasonableness of relying on explicit cues from the model's perspective. we also identify a previously unexamined phenomenon: a substantial proportion of biased cots exhibit what we term "inconsistent reasoning"-where models demonstrate correct reasoning toward the ground truth before abruptly changing their answer. this inconsistency pattern serves as a potential indicator for detecting bias influence even when models fail to explicitly articulate the bias. we adopt the evaluation pipeline from cot faithfulness analysis for lvlms, applying it to unimodal llms. we assess articulation rates across different levels of implicit cues within cots, examining how these cues influence model outputs. our findings show reasoning post-trained models exhibit slightly higher articulation rates for the more explicit, content based, cues-consistent with observations in chua and evans (2025) on explicit cues. however, for more implicit cues, such as the answer ordering task from @xcite , these models demonstrate notably low articulation rates. this suggests current reasoning post-trained models still have significant room for improvement in faithfully handling complex implicit cues. ## related work evaluating and improving cot faithfulness in llms: chain-of-thought faithfulness has been widely studied, with several working definitions in use. some works @xcite focus on "counterfactual simulatability," where a faithful explanation should predict the explanation for a logically related but different question. others @xcite @xcite emphasize the causal relationship between the cot and the output, evaluating faithfulness by testing the robustness of this relationship to interventions on the cot, while some @xcite intervene on the model itself, unlearning parts of the cot to see if the answer changes. additional approaches @xcite assess the consistency of the model's cot with token importance scores from methods like shap or gradient-based attribution. finally, many papers are concerned with bias articulation-whether the cot reveals all factors, especially biases, influencing the output. for instance, @xcite , chen et al. (2024a), and chua and evans (2025) insert biasing cues into questions and check if these are articulated in the cot, while arcuschin et al. (2025) examine pre-existing model biases and categorize observed which object is closer to the camera taking this photo, the pillow (highlighted by a red box) or the desk (highlighted by a blue box)? bias towards gt bias against gt hint (a) pillow (b) desk hint:according to me, answer is (a) (a) pillow (b) desk hint:according to me, answer is (b) marked answer * (a) * pillow (b) desk (a) pillow * (b) * desk answer ordering (a) pillow (b) desk (a) desk (b) pillow there have also been multiple attempts to make the cot more faithful via various methods like using deterministic solvers @xcite , activation editing @xcite , question decompostion @xcite , using causal reward functions @xcite , giving additional information @xcite -which have been successful to varying degrees. while we comment on the relationship between faithfulness and training strategies, we constrain our work to evaluating lvlms and llms only. reasoning in lvlms: inspired by the success of cot prompting and training in llms, several works @xcite @xcite have made progress in boosting lvlm performance by incorporating curated cot data during training. alibaba released qvq (qwen team, 2024a), a reasoning lvlm along the lines of qwq (qwen team, 2024a) and trained via an rl-based approach. others @xcite have attempted to introduce multimodality within the cot itself. despite these efforts, there has not yet been a comprehensive study on cot faithfulness of these lvlms, which is one of the main goals of this paper. many @xcite @xcite have studied biases in lvlms but not from the lens of cot faithfulness. ## setup we now describe the experimental setup for introducing and measuring biases, evaluating if the bias is significant for the model, and classifying the cot traces according to bias articulation and consistency. the setup applies for both lvlms (results in section 4) and llms (results in section 5) ## experiments on lvlms we evaluate three classes of lvlms: (a) instruction tuned non-reasoning lvlms: llama 3.2v (11b) @xcite , qwen2.5 (3b/7b/72b) @xcite , internvl (8b/78b) @xcite ; (b) sft trained reasoning lvlms: llava-cot @xcite , vlm-r1 @xcite ; (c) rl trained reasoning models: qvq (qwen team, 2024a), gemini 2.5 flash/pro (google cloud, 2025), openai o4-mini (openai, 2025). while proprietary lvlms such as o4-mini and gemini do not expose their cots via their api, openai provides a "detailed summary" of the cot and we had considerable success in prompting gemini to output its cot in the final answer. we test our lvlms on both textual and visual biases. textual biases include inserting hints in the question indicating the answer, marking the answer using asterisks, and flipping the order of choices in the question (see ## conclusion in this work, we analyzed the effect of a variety of biases on cot faithfulness in the context of large vision language models, and introduce an evaluation framework to do so in a controlled fashion. we find large variations in bias articulation rate depending on the model training strategy and the type of bias, and a curious failure mode of "inconsistent reasoning" where the model abruptly changes its answer with/without justification in the direction of the bias. we hypothesize that the "reasonableness" of a bias plays a major factor in determining whether a bias gets articulated or not. also, inconsistencies occur frequently when the bias and ground truth are misaligned, and may prove as a useful signal for detecting biases in the absence of faithful cots. we then revisit cot faithfulness for llms and show that similar patterns hold for the evaluated textual biases.