Papers by Junxiong Zhu

2 papers
MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples (2025.coling-main)

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Challenge: Existing preference optimization methods such as DPO and KTO are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data.
Approach: They propose an algorithm that leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data.
Outcome: The proposed algorithm outperforms DPO, ORPO, and SimPO on MT-Bench and Arena-Hard.
MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning (2026.findings-acl)

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Challenge: Large Language Models (LLMs) exhibit severe hallucinations, which undermine reliability of automated scientific document understanding systems.
Approach: They propose a framework for mitigating scientific measurement hallucinations through enhanced reasoning and targeted optimization.
Outcome: The proposed framework significantly reduces hallucination rates and improves overall accuracy on the MeasEval benchmark.

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