Papers by Xuyang Wu
Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts (2025.findings-emnlp)
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| Challenge: | Large vision-language models have demonstrated strong capabilities in open-world visual understanding, but it is not clear how they address demographic biases in real life. |
| Approach: | They propose a method to assess visual fairness in LVLMs by question-answering/classification tasks. |
| Outcome: | The proposed approach improves transparency and offers a scalable solution for fairness mitigation. |
Does Reasoning Introduce Bias? A Study of Social Bias Evaluation and Mitigation in LLM Reasoning (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models have enabled automatic generation of chain-of-thought reasoning . however, when reasoning steps reflect social stereotypes, they can reinforce harmful associations and lead to misleading conclusions. |
| Approach: | They propose a method that detects how model predictions change across incremental reasoning steps. |
| Outcome: | The proposed method outperforms a stereotype-free baseline and improves accuracy. |
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025.coling-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors. |
| Approach: | They propose a framework to evaluate fairness in RAG using scenario-based questions and analyzing disparities across demographic attributes. |
| Outcome: | The proposed framework analyzes disparities across demographic attributes and identifies fairness issues in retrieval and generation stages. |
Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers (2024.naacl-long)
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| Challenge: | Recent studies have shown that Large Language Models (LLMs) are more efficient in natural language understanding tasks. |
| Approach: | They evaluate large language models (LLMs) using a TREC Fair Ranking dataset . they assess fairness from both user and content perspectives . |
| Outcome: | The proposed model outperforms the existing models in the fair ranking task. |