Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment (2025)

Abstract: A binary decision task, like yes-no questions or answer verification, reflects a significant real-world scenario such as where users look for confirmation about the correctness of their decisions on specific issues. In this work, we observe that language models exhibit a negative bias in the binary decisions of complex reasoning tasks. Based on our observations and the rationale about attention-based model dynamics, we propose a negative attention score (NAS) to systematically and quantitatively formulate negative bias. Based on NAS, we identify attention heads that attend to negative tokens provided in the instructions as answer candidate of binary decisions, regardless of the question in the prompt, and validate their association with the negative bias. Additionally, we propose the negative attention score alignment (NASA) method, which is a parameter-efficient fine-tuning technique to address the extracted negatively biased attention heads. Experimental results from various domains of reasoning tasks and large model search space demonstrate that NASA significantly reduces the gap between precision and recall caused by negative bias while preserving their generalization abilities.

## introduction recent advancements in large language models (llms) have enabled complex reasoning tasks executed by understanding user instructions @xcite @xcite . as the capabilities of llms have expanded rapidly, research has intensified to analyze their characteristics and inherent issues. one of the major issues is the generation of factually incorrect content, known as "hallucination", which significantly degrades the reliability of llm-based services @xcite @xcite . ## negative bias in llms a binary decision task, such as yes-no qa or answer verification, represents a major real-world scenario where users seek confirmation on whether their decisions regarding specific problems are correct. for instance, a user may inquire whether a statement formed through various contexts is true. also, a user may seek validation of the correctness of their proposed solutions to problems requiring complex logical reasoning. such fundamental interactions, represented as yes-no decisions, act as the basic block of the high-dimensional reasoning tasks that need step-by-step sub-tasks. despite this significance of the binary decision task, many complex reasoning benchmark datasets are not structured in a binary decision format. in this section, we first transform existing complex reasoning tasks into yes-no binary decision tasks. we then conduct statistical observations and quantitative analyses to examine how llms perform in these binary decision scenarios, observing a meaningful negative bias in their decisions. following this, we propose a systematic framework, the negative attention score (nas), to formulate and quantify the negative bias exhibited by llms. ## nasa: nas alignment framework motivated by previous observations, we propose nas alignment (nasa), a framework designed to address attention heads that induce negative bias in a parameter-efficient manner. in nasa, we start by constructing a probing set to identify negative attention heads, followed by a selection process to determine which of these heads will be fine-tuned. our framework fine-tunes the target attention heads in the order based on single head nas. ## analysis we analyze the impact of nasa on the general reasoning capability of llms beyond the binary decision task which is the primary focus of this work. after that, we examine the effect of nasa on model calibration. additionally, regarding performance improvement and negative bias, we investigate the relationship between few-shot examples and nasa in few-shot prompting scenarios. we present an instruction following analysis and an ablation study in appendices i, j and k. ## discussion through our experiments, we identify two key characteristics of negative bias. model nas may not negatively correlate with f1 score during fine-tuning as shown in accurate identification of negative attention heads is crucial, and nas facilitates this process our ablation study in ## conclusion we identify a critical issue where large language models exhibit negative bias in binary decision tasks requiring complex reasoning. to address this issue, we propose a negative attention score and employ it to discover query-agnostic negative heads. by performing parameter-efficient tuning on these heads, we introduce the nasa method, which effectively mitigates the bias problem. our method not only enhances the performance of the model but also serves as a useful analytical framework from the perspective of interpretability.