论文链接:A Survey on Evaluation of Large Language Models 大模型综述 介绍 关注三个关键问题:要评估什么,在哪里要评估,以及如何评估 人们普遍认为,真实的智力使我们具备推理能力,使我们能够测试假设,并为未来的现实发展做好准备。 评价LLM的重要性 评价LLM帮助我们更好地了解LLM的优点和缺点 更好的评估可以为人类...
【1】Holistic Evaluation of Language Models，论文地址：https://arxiv.org/abs/2211.09110
摘要:该文为大模型评估方向的综述论文。 本文分享自华为云社区《【论文分享】《Holistic Evaluation of Language Models》》,作者:DevAI。 大模型(LLM)已经成为了大多数语言相关的技术的基石,然而大模型的能力、限制、风险还没有被大家完整地认识。该文为大模型评估方向的综述论文,由Percy Liang团队打造,将2022年四...
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task...
With the rise of Large Language Models (LLMs) and their ubiquitous deployment in diverse domains, measuring language model behavior on realistic data is imperative. For example, a company deploying a client-facing chatbot must ensure that
Compression is believed to be the key feature of intelligence. Llm-compressive allows you to evaluate Large Language Models (LLMs) for generalization and robustness viadata compression. Llm-compressive tests LLMs with data compression on timeline, to understand how LLMs generalize over time. ...
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This project provides a unified framework to test generative language models on a large number of different evaluation tasks. Features: Over 60 standard academic benchmarks for LLMs, with hundreds of subtasks and variants implemented. Support for models loaded viatransformers(including quantization via...
Specifically, the research aims to go beyond standard benchmarks to more effectively evaluate various types of large language models (LLMs), from foundational models to those fine-tuned for specific tasks. The goal is to move toward real-world, evidence-based performance guarantees and ...
玄野 大模型(LLM)最新论文摘要 | Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization Authors: Yixin Liu, Alexander R. Fabbri, Jiawen Chen, Yilun Zhao, Simeng Han, Shafiq Joty, Pengfei Liu, Dragomir Radev, Chien-Sheng Wu, Arman ...