| Challenge: | Existing LLMs suffer from biases and misalignment due to limited functional understanding and knowledge gaps. |
| Approach: | They introduce a framework that leverages a criteria planner model and optimized machine metrics to enhance the scalability and fairness of LLM-based evaluation. |
| Outcome: | The proposed framework reduces biases and improves alignment with human preferences, with gains of up to 0.324 in Spearman correlation. |
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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
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Fusion-Eval: Integrating Assistant Evaluators with LLMs (2024.emnlp-industry)
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| Challenge: | Recent studies have employed large language models (LLMs) as reference-free metrics for NLG evaluation, enhancing adaptability to new tasks tasks. |
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