{"id":64996,"date":"2025-02-11T15:44:56","date_gmt":"2025-02-11T14:44:56","guid":{"rendered":"https:\/\/dataconomy.ru\/?p=64996"},"modified":"2025-02-11T15:44:56","modified_gmt":"2025-02-11T14:44:56","slug":"llm-performance-scores-are-inflated-a-new-method-shows-the-truth","status":"publish","type":"post","link":"https:\/\/dataconomy.ru\/2025\/02\/11\/llm-performance-scores-are-inflated-a-new-method-shows-the-truth\/","title":{"rendered":"LLM performance scores are inflated: A new method shows the truth"},"content":{"rendered":"

As large language models (LLMs) become increasingly sophisticated, ensuring fair and unbiased evaluation has become a critical challenge. Existing evaluation protocols often suffer from benchmark contamination<\/strong>, where models are trained on datasets that include portions of the test benchmarks, leading to artificially inflated results. A recent approach known as Agents-as-an-Evaluator<\/strong> attempts to address this issue by generating new test questions using AI agents. However, this method introduces its own biases<\/strong>, which remain largely unexplored.<\/p>\n

Researchers from Hikvision Research Institute, including Meilin Chen, Jian Tian, Liang Ma, Di Xie, Weijie Chen, and Jiang Zhu, propose a new evaluation framework called the Unbiased Evaluator in their study, “Unbiased Evaluation of Large Language Models from a Causal Perspective<\/em><\/a>,” to mitigate these biases.<\/p>\n

Their study provides a theoretical framework for evaluation bias<\/strong> and introduces a causality-based evaluation protocol<\/strong> to offer a more comprehensive, unbiased, and interpretable<\/strong> assessment of LLMs.<\/p>\n

Challenges with Agents-as-an-Evaluator<\/h2>\n

While Agents-as-an-Evaluator<\/strong> attempts to reduce benchmark contamination by having AI-generated test questions, the researchers identify two key biases in this method:<\/p>\n

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  1. Data bias<\/strong>: AI-generated test questions tend to favor domains where the model already performs well<\/strong>, leading to an unbalanced assessment.<\/li>\n
  2. Model bias<\/strong>: During evaluation, AI-generated content aligns more with the model\u2019s strengths, giving it an unfair advantage<\/strong> when assessing itself.<\/li>\n<\/ol>\n

    These biases distort the evaluation process, making it difficult to accurately measure a model\u2019s true capabilities.<\/p>\n

    Introducing the Unbiased Evaluator<\/h2>\n

    To address these issues, the researchers introduce the Unbiased Evaluator<\/strong>, an evaluation protocol based on causal inference principles<\/strong>. This method dynamically evaluates LLMs using controlled interventions<\/strong>, rather than relying solely on static datasets.<\/p>\n

    At its core, the Unbiased Evaluator utilizes Bags of Atomic Interventions (BOAT)<\/strong>\u2014structured manipulations of test data to assess how LLMs respond to different variations of the same question. This method allows for a systematic evaluation of AI robustness<\/strong>, reducing the impact of pre-existing biases.<\/p>\n

    Testing the theory: Human, AI, and recursive oversight experiments<\/h2>\n

    To validate their hypotheses, the researchers conducted a series of experiments involving:<\/p>\n