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我们对LLMs的期望是什么？LLM基准设计的映射What Do We Expect from LLMs? Mapping the Design of LLM Benchmarks

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

最近的研究强调了大型语言模型（LLMs）评估基准的不断演变，强调向行动和互动指标的转变。对14,767篇arXiv研究论文的分析表明，尽管这些基准的扩展旨在提供对LLM能力的更全面理解，但人们担心它们可能只是强化现有偏见，而不是提供新的见解。这引发了关于当前评估方法在评估LLM性能方面的有效性和公平性的重要问题。

Recent research has highlighted the evolving landscape of evaluation benchmarks for large language models (LLMs), emphasizing a shift towards action and interaction metrics. An analysis of 14,767 research papers from arXiv indicates that while the expansion of these benchmarks aims to provide a more comprehensive understanding of LLM capabilities, there are concerns that they may simply reinforce existing biases rather than offering new insights. This raises important questions about the effectiveness and fairness of current evaluation methods in assessing LLM performance.

要点

  1. LLMs的评估基准越来越关注行动和互动。

    The evaluation benchmarks for LLMs are increasingly focusing on action and interaction.

  2. 分析了大量研究论文以了解LLM基准的趋势。

    A significant number of research papers have been analyzed to understand the trends in LLM benchmarking.

  3. 新基准可能无法解决LLMs中的偏见，反而可能延续这些偏见。

    There is a risk that new benchmarks may not address biases in LLMs but rather perpetuate them.

为何重要

LLM基准的演变对于确保这些模型得到公平和有效的评估至关重要，这可能影响它们在现实应用中的部署。理解这些基准的影响可以促进更负责任的AI开发。

The evolution of LLM benchmarks is crucial for ensuring that these models are evaluated fairly and effectively, which can impact their deployment in real-world applications. Understanding the implications of these benchmarks can lead to more responsible AI development.

按谁的角度看

产品经理

对于产品经理来说，了解LLM基准对于做出基于数据的模型选择和部署决策至关重要，以确保产品满足用户需求而不延续偏见。

For product managers, staying informed about LLM benchmarks is essential for making data-driven decisions about model selection and deployment, ensuring that products meet user needs without perpetuating biases.

开发者

开发者和工程师应了解不断演变的基准，以更好地理解如何优化LLMs的性能和公平性，从而指导其在应用中的实施。

Developers and engineers should be aware of the evolving benchmarks to better understand how to optimize LLMs for performance and fairness, guiding their implementation in applications.

学生与求职者

对于学生和求职者来说，了解LLM基准趋势可以在理解AI评估方法方面提供竞争优势，这在就业市场中越来越相关。

For students and job-seekers, knowledge of LLM benchmarking trends can provide a competitive edge in understanding AI evaluation methods, which is increasingly relevant in the job market.

覆盖来源

来源标题与摘录保留原文，以便核对与追溯。

  • arXiv cs.AI

    What Do We Expect from LLMs? Mapping the Design of LLM Benchmarks

    The article reveals that the evaluation benchmarks for large language models (LLMs) are evolving, with a focus on action and interaction, based on an analysis of 14,767 research papers from arXiv. It raises concerns about whether the expansion of these benchmarks provides new insights or merely reinforces existing biases.