Model Evaluation Using a Panel of Large Language Models Evaluators (PoLL)
Addressing Challenges in Large Language Models (LLMs)
Large Language Models (LLMs) are advancing rapidly, but the lack of adequate data for thorough verification poses a challenge. Evaluating the precision and quality of a model’s text production is complex.
Practical Solutions and Value
Evaluations now use LLMs as judges to score other models, such as GPT-4, but this approach has drawbacks, including high costs and potential bias. An alternative is using a Panel of LLM evaluators (PoLL) with smaller models, which has shown superior performance and cost-effectiveness.
Benefits of PoLL
The PoLL framework reduces intra-model bias and offers cost-saving advantages, making evaluations more precise and economical.
Research Findings
The research has demonstrated the effectiveness of PoLL with various datasets and settings, showing that it is more cost-effective and closely correlates with human evaluations compared to using a single large judge like GPT-4.
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