Mitigating Bias in LLMs via EquiSync: A Multi-Objective Optimization Perspective
Authors: Zhenjie Xu, Wenqing Chen, Yi Tang, Xuanying Li, Cheng Hu, Zibin Zheng, Zhichao Lu


Reviewer paper summaries: The paper propose EquiSync, a multiagent framework to enhance fairness of LLM reasoning.
Summary of strengths: 1. The idea of debiasing using multiple LLM agents with different profiles is interesting and novel
2. The paper first identify the tarde-off between the downstream task performance and fairness, and then proposes the multi-agent framework to mitigate the bias, is interesting
Summary of weaknesses: 1. Even though the idea of the paper is interesting, the method proposed in this paper lacks technical depth. In essence it's like an ensemble of LLMs with different profiles (prompts). Also, self-consistency should be considered as a baseline to rectify the bias.

2. The applicability of the framework is doubted. For one single query, a simplest baseline just needs to query an LLM once. Through such multi-agent framework, to achieve the better performance-fairness trade-off, how many LLM agents are needed? how many rounds of communication is needed? Will this result in huge time and space complexity? What is the scaling law of the complexity and the trade-off? Those are the key analyses of the applicability of the framework but are not discussed. 

3. Some description of the writing is too dense and redundant in section 3.
Comments, suggestions and typos: 1. Can the author compare their method with self-consistency with LLMs given different personas / profiles?

2. Can the author analyze the scaling law of the proposed method is demonstrated the cost and applicability of the framework?

3. Can the author improve the writing in section 3 by removing those redundant descriptions?

4. The current title is not accurate. It does not reflect the multi-agent essence of the framework.
Overall assessment scores: 2