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Kunquan Li

2 accepted papers

2026

Bi-directional Bias Attribution: Debiasing Large Language Models without Modifying Prompts

ICLR 2026poster

Large language models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. However, their outputs often exhibit social biases, raising fairness concerns. Existing debiasing methods, such as fine-tuning on additional datasets or prompt engineering…

Cited by 0SourcecodeScholar
2025

A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection

EMNLP 2025

Misinformation spans various domains, but detection methods trained on specific domains often perform poorly when applied to others. With the rapid development of Large Language Models (LLMs), researchers have begun to utilize LLMs for cross-domain misinformation detection. However, existing LLM-bas

Cited by 0SourcePDFScholar