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Self-Taught Evaluators
🔄 Self-Taught Evaluators
This research paper explores the development of self-taught language model evaluators. Instead of relying on costly human annotations, this approach utilizes synthetic data generated by the model itself. The method iteratively trains an LLM-as-a-Judge by creating contrasting response pairs, generating reasoning traces, and fine-tuning the model on this synthetic data. The research demonstrates that this method significantly improves the accuracy of the evaluator on benchmarks like RewardBench, achieving performance comparable to reward models trained with labeled examples. The authors also explore various data sources, ablations, and analyses to understand the effectiveness of the proposed approach.
📎 Link to paper
🌐 Link to their tweet
This research paper explores the development of self-taught language model evaluators. Instead of relying on costly human annotations, this approach utilizes synthetic data generated by the model itself. The method iteratively trains an LLM-as-a-Judge by creating contrasting response pairs, generating reasoning traces, and fine-tuning the model on this synthetic data. The research demonstrates that this method significantly improves the accuracy of the evaluator on benchmarks like RewardBench, achieving performance comparable to reward models trained with labeled examples. The authors also explore various data sources, ablations, and analyses to understand the effectiveness of the proposed approach.
📎 Link to paper
🌐 Link to their tweet
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By Shahriar Shariati🔄 Self-Taught Evaluators
This research paper explores the development of self-taught language model evaluators. Instead of relying on costly human annotations, this approach utilizes synthetic data generated by the model itself. The method iteratively trains an LLM-as-a-Judge by creating contrasting response pairs, generating reasoning traces, and fine-tuning the model on this synthetic data. The research demonstrates that this method significantly improves the accuracy of the evaluator on benchmarks like RewardBench, achieving performance comparable to reward models trained with labeled examples. The authors also explore various data sources, ablations, and analyses to understand the effectiveness of the proposed approach.
📎 Link to paper
🌐 Link to their tweet
This research paper explores the development of self-taught language model evaluators. Instead of relying on costly human annotations, this approach utilizes synthetic data generated by the model itself. The method iteratively trains an LLM-as-a-Judge by creating contrasting response pairs, generating reasoning traces, and fine-tuning the model on this synthetic data. The research demonstrates that this method significantly improves the accuracy of the evaluator on benchmarks like RewardBench, achieving performance comparable to reward models trained with labeled examples. The authors also explore various data sources, ablations, and analyses to understand the effectiveness of the proposed approach.
📎 Link to paper
🌐 Link to their tweet