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Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models

Apr 30, 2024 - news.bensbites.com
The article discusses the challenges in evaluating the quality of Large Language Models (LLMs) due to their advanced nature and the difficulty in finding adequate data to probe their properties. The current evaluation method, which uses a single large model like GPT4, is found to be costly and introduces intramodel bias. The authors propose an alternative method, using a Panel of LLm evaluators (PoLL), which they argue is more effective and less expensive.

The authors found that a PoLL, composed of a larger number of smaller models, outperforms a single large judge in terms of quality evaluation. This method also exhibits less intra-model bias due to the composition of disjoint model families. The study, conducted across three distinct judge settings and six different datasets, revealed that the PoLL method is over seven times less expensive than the current method.

Key takeaways:

  • Large Language Models (LLMs) have become so advanced that accurately evaluating their quality is challenging.
  • Many evaluations now use LLMs themselves as judges to score the quality of outputs from other LLMs, which can be costly and introduce intramodel bias.
  • The authors propose to evaluate models using a Panel of LLm evaluators (PoLL), which they find to be more effective and less biased.
  • The PoLL method is over seven times less expensive than using a single large model for evaluation.
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