SCIENCE@home

DeFrame: Debiasing Large Language Models Against Framing Effects

Kahee Lim,Soyeon Kim,Steven Euijong Whang

Published 2026 in Unknown venue

ABSTRACT

As large language models (LLMs) are increasingly deployed in real-world applications, ensuring their fair responses across demographics has become crucial. Despite many efforts, an ongoing challenge is hidden bias: LLMs appear fair under standard evaluations, but can produce biased responses outside those evaluation settings. In this paper, we identify framing -- differences in how semantically equivalent prompts are expressed (e.g.,"A is better than B"vs."B is worse than A") -- as an underexplored contributor to this gap. We first introduce the concept of"framing disparity"to quantify the impact of framing on fairness evaluation. By augmenting fairness evaluation benchmarks with alternative framings, we find that (1) fairness scores vary significantly with framing and (2) existing debiasing methods improve overall (i.e., frame-averaged) fairness, but often fail to reduce framing-induced disparities. To address this, we propose a framing-aware debiasing method that encourages LLMs to be more consistent across framings. Experiments demonstrate that our approach reduces overall bias and improves robustness against framing disparities, enabling LLMs to produce fairer and more consistent responses.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    Unknown venue

  • Publication date

    2026-02-04

  • Fields of study

    Linguistics, Computer Science

  • Identifiers
    arXiv 2602.04306
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-50 of 50 references · Page 1 of 1

CITED BY

  • No citing papers are available for this paper.

Showing 0-0 of 0 citing papers · Page 1 of 1