Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning

• URL: http://arxiv.org/abs/2504.05632v3
• Date: Thu, 05 Jun 2025 21:56:19 GMT
• Title: Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning
• Authors: Sanchit Kabra, Akshita Jha, Chandan K. Reddy,
• Abstract summary: We investigate the crucial relationship between a model's reasoning ability and fairness.<n>We find that larger models with stronger reasoning abilities exhibit substantially lower stereotypical bias.<n>We introduce ReGiFT, a novel approach that extracts structured reasoning traces from advanced reasoning models and infuses them into models that lack such capabilities.
• Score: 12.559028963968247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large-scale generative language models have shown that reasoning capabilities can significantly improve model performance across a variety of tasks. However, the impact of reasoning on a model's ability to mitigate stereotypical responses remains largely underexplored. In this work, we investigate the crucial relationship between a model's reasoning ability and fairness, and ask whether improved reasoning capabilities can mitigate harmful stereotypical responses, especially those arising due to shallow or flawed reasoning. We conduct a comprehensive evaluation of multiple open-source LLMs, and find that larger models with stronger reasoning abilities exhibit substantially lower stereotypical bias on existing fairness benchmarks. Building on this insight, we introduce ReGiFT -- Reasoning Guided Fine-Tuning, a novel approach that extracts structured reasoning traces from advanced reasoning models and infuses them into models that lack such capabilities. We use only general-purpose reasoning and do not require any fairness-specific supervision for bias mitigation. Notably, we see that models fine-tuned using ReGiFT not only improve fairness relative to their non-reasoning counterparts but also outperform advanced reasoning models on fairness benchmarks. We also analyze how variations in the correctness of the reasoning traces and their length influence model fairness and their overall performance. Our findings highlight that enhancing reasoning capabilities is an effective, fairness-agnostic strategy for mitigating stereotypical bias caused by reasoning flaws.

Related papers

• Guiding LLM Decision-Making with Fairness Reward Models [12.32062012708603]
  Large language models are increasingly used to support high-stakes decisions.<n>We propose a framework for training a generalizable Fairness Reward Model.<n>We show that our approach consistently improves fairness while matching, or even surpassing, baseline accuracy.
  arXiv  Detail & Related papers  (2025-07-15T14:20:23Z)
• Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models [0.0]
  Reasoning Language Models (RLMs) have gained traction for their ability to perform complex, multi-step reasoning tasks.<n>While these capabilities promise improved reliability, their impact on robustness to social biases remains unclear.<n>We leverage the CLEAR-Bias benchmark to investigate the adversarial robustness of RLMs to bias elicitation.
  arXiv  Detail & Related papers  (2025-07-03T17:01:53Z)
• NaturalThoughts: Selecting and Distilling Reasoning Traces for General Reasoning Tasks [65.70224757972068]
  We select reasoning traces from a strong teacher model based on a large pool of questions from NaturalReasoning.<n>We find that simply scaling up data size with random sampling is a strong baseline with steady performance gains.<n>We find that selecting difficult examples that require more diverse reasoning strategies is more sample-efficient to transfer the teacher model's reasoning skills.
  arXiv  Detail & Related papers  (2025-07-02T17:30:24Z)
• Lost at the Beginning of Reasoning [82.18834329384514]
  We show that the first reasoning step exerts a disproportionately large influence on the final prediction.<n>We propose an efficient sampling strategy that leverages a reward model to identify and retain high-quality first reasoning steps.<n>We introduce a new benchmark specifically constructed with deliberately flawed first reasoning steps to systematically evaluate model self-correction capabilities.
  arXiv  Detail & Related papers  (2025-06-27T09:53:57Z)
• Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability [70.4107059502882]
  Training language models with rationales augmentation has been shown to be beneficial in many existing works.<n>We conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance.
  arXiv  Detail & Related papers  (2025-05-30T02:39:37Z)
• A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models [53.18562650350898]
  Chain-of-thought (CoT) reasoning enhances performance of large language models.<n>We present the first comprehensive study of CoT faithfulness in large vision-language models.
  arXiv  Detail & Related papers  (2025-05-29T18:55:05Z)
• Thinking Out Loud: Do Reasoning Models Know When They're Right? [19.776645881640178]
  Large reasoning models (LRMs) have recently demonstrated impressive capabilities in complex reasoning tasks.<n>We investigate how LRMs interact with other model behaviors by analyzing verbalized confidence.<n>We find that reasoning models may possess a diminished awareness of their own knowledge boundaries.
  arXiv  Detail & Related papers  (2025-04-09T03:58:19Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Reasoning Elicitation in Language Models via Counterfactual Feedback [17.908819732623716]
  We derive novel metrics that balance accuracy in factual and counterfactual questions.<n>We propose several fine-tuning approaches that aim to elicit better reasoning mechanisms.<n>We evaluate the performance of the fine-tuned language models in a variety of realistic scenarios.
  arXiv  Detail & Related papers  (2024-10-02T15:33:30Z)
• Conceptual and Unbiased Reasoning in Language Models [98.90677711523645]
  We propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions. We show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks. We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making.
  arXiv  Detail & Related papers  (2024-03-30T00:53:53Z)
• Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models [100.53662473219806]
  Diffusion-of-Thought (DoT) is a novel approach that integrates diffusion models with Chain-of-Thought.<n>DoT allows reasoning steps to diffuse over time through a diffusion language model.<n>Our results demonstrate the effectiveness of DoT in multi-digit multiplication, logic, and grade school math problems.
  arXiv  Detail & Related papers  (2024-02-12T16:23:28Z)
• A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning [73.77088902676306]
  We take a closer look at the self-verification abilities of large language models (LLMs) in the context of logical reasoning. Our main findings suggest that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self-verification methods.
  arXiv  Detail & Related papers  (2023-11-14T07:13:10Z)
• Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities [17.082695183953486]
  A popular workaround is to train a robust model by re-weighting training examples based on a secondary biased model. Here, the underlying assumption is that the biased model resorts to shortcut features. We introduce a fine-tuning strategy that incorporates the similarity between the main and biased model attribution scores in a Product of Experts loss function.
  arXiv  Detail & Related papers  (2023-02-06T15:21:41Z)
• Fairness Increases Adversarial Vulnerability [50.90773979394264]
  This paper shows the existence of a dichotomy between fairness and robustness, and analyzes when achieving fairness decreases the model robustness to adversarial samples. Experiments on non-linear models and different architectures validate the theoretical findings in multiple vision domains. The paper proposes a simple, yet effective, solution to construct models achieving good tradeoffs between fairness and robustness.
  arXiv  Detail & Related papers  (2022-11-21T19:55:35Z)
• Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations [97.91630330328815]
  We conduct a crowdsourcing study, where participants interact with deception detection models that have been trained to distinguish between genuine and fake hotel reviews. We observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control.
  arXiv  Detail & Related papers  (2021-12-17T18:29:56Z)
• CausaLM: Causal Model Explanation Through Counterfactual Language Models [33.29636213961804]
  CausaLM is a framework for producing causal model explanations using counterfactual language representation models. We show that language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest. A byproduct of our method is a language representation model that is unaffected by the tested concept.
  arXiv  Detail & Related papers  (2020-05-27T15:06:35Z)

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.