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Author critiques own LLM judges using rigorous evaluation exercises

By PulseAugur Editorial · [1 sources] ·

The author details their process of evaluating the effectiveness of two custom Large Language Model (LLM) judges they developed. They applied a method inspired by Dan Luu, which involves identifying flaws in benchmarks before seeing the explanation. The author presents five exercises derived from their LLM judges' performance data, highlighting inconsistencies and areas for improvement in accuracy and precision. These exercises reveal issues with the judges' reliability and the impact of prompt changes, even when using the same underlying model like Haiku 4.5 or Sonnet 5. AI

IMPACT Provides insights into practical methods for evaluating and improving LLM-based tools, relevant for developers and researchers.

RANK_REASON The item is a personal blog post detailing the author's self-evaluation of custom-built LLM tools, rather than a release of new technology or research.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Author critiques own LLM judges using rigorous evaluation exercises

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The item is a personal blog post detailing the author's self-evaluation of custom-built LLM tools, rather than a release of new technology or research.
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  1. dev.to — LLM tag TIER_1 English(EN) · Alessandro Prandini ·

    Bad evals, my own: five exercises from two LLM judges

    <p>I run two small LLM judges. One reads ~200 items a day from AI feeds and tells me which five to read (I call it brief). The other reads Reddit threads and tells me which ones are worth a comment from me (scout). Both have a regression suite, both have a promoted baseline, both…

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