Papers by Jiamei Zheng

2 papers
MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling (2025.findings-emnlp)

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Challenge: MT-RewardTree provides a framework for constructing, evaluating, and deploying process reward models in machine translation (MT)
Approach: They propose a method for automatically generating token-level preference pairs using approximate Monte Carlo Tree Search.
Outcome: The proposed framework achieves state-of-the-art performance in token-level evaluation and sequence-level analysis.
M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation (2025.acl-long)

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Challenge: Recent advances in large language models (LLMs) have shown their potential to deliver human-like judgments.
Approach: They propose a systematic LLM-based multi-agent framework for advanced LLM as-a-judge MT evaluation that integrates dimension-specific results into a final evaluation judgment.
Outcome: The proposed framework outperforms existing LLM-as-a-judge methods and competes with state-of-the-art automatic metrics even when powered by a suboptimal model like GPT-4o mini.

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