The researchers propose JudgeLM, a scalable language model judge designed to evaluate large language models (LLMs) in open-ended scenarios. They introduce a high-quality dataset for judge models, examine biases in LLM judge fine-tuning, and provide solutions. JudgeLM shows increased consistency and adaptability over various scenarios. The dataset serves as a foundation for future research on LLM evaluation.
**This AI Paper Introduces JudgeLM: A Novel Approach for Scalable Evaluation of Large Language Models in Open-Ended Scenarios**
Large language models (LLMs) have gained attention for their ability to follow instructions and handle various scenarios. However, their performance in open-ended situations needs to be properly assessed. This paper proposes a new benchmark approach called JudgeLM, which evaluates LLMs thoroughly in open-ended activities.
JudgeLM is a scalable language model judge designed to evaluate LLMs. It combines a high-quality dataset for training and assessing judge models with scalable judges acting as evaluators. The researchers modify open-source LLMs to serve as judges and examine their performance in terms of model size and training data volume.
To overcome biases in LLMs used as judges, the researchers provide techniques such as reference drop, reference support, and swap augmentation. They also introduce additional features to the JudgeLM system, including multi-turn conversation, grading single replies, and judging multiple answers.
Compared to other approaches, JudgeLM is a quick and cost-effective solution. It offers more privacy protection and repeatability than closed-source LLM judges. The dataset presented in the paper is comprehensive and superior, providing valuable insights for future research.
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