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In this section, the paper discusses the evaluation methods and libraries used to assess the performance of large language models (LLMs) in handling complex tasks. Specifically, it focuses on the evaluation of LLMs in three areas: natural language understanding, mathematical reasoning, and tool learning.

In natural language understanding, the evaluation of LLMs involves various tasks such as text completion, sentiment analysis, and question answering. The paper discusses existing evaluation datasets such as GLUE, SuperGLUE, and SQuAD, which consist of labeled examples that can be used to assess the performance of LLMs. It also highlights the adoption of evaluation metrics such as accuracy, F1 score, and ROUGE-L.

In mathematical reasoning, evaluation datasets are designed to test LLMs’ ability to reason, abstract, and calculate mathematical problems. The paper describes the two stages of development in mathematical reasoning evaluation datasets. The first stage predates the advent of LLMs and focuses on automated solutions for mathematics and science problems. The second stage involves the creation of datasets specifically curated for evaluating LLMs. The paper discusses datasets such as M3KE, C-EVAL, VNHSGE, MATH, and JEEBench, which cover various mathematical concepts and problem types. The evaluation of LLMs in mathematical reasoning often involves zero-shot or few-shot settings, where models are tested on problems with minimal or no examples provided in the prompts. The paper also explores different prompting methods used in mathematical reasoning evaluation, such as chain-of-thought prompting and self-consistency prompting.

In tool learning, LLMs are trained to manipulate tools and interact with the real world. The paper categorizes the evaluation methods for tool learning into two categories: tool-augmented learning and tool-oriented learning. Tool-augmented learning involves using tools to enhance or expand the model’s abilities, while tool-oriented learning focuses on mastering specific tools or techniques. The paper discusses evaluation methods for both categories, including assessing whether the model can successfully execute tools and assessing the quality of generated programs. It also highlights the need for evaluation metrics and benchmarks that can accurately assess the performance of LLMs in tool learning tasks. The paper introduces several benchmark datasets for tool learning evaluation, such as WebCPM for search engine manipulation, WebShop for online shopping, RoboCodeGen for code generation, and ALFWorld for robotic tasks. It also discusses the evaluation of LLMs in multi-tool scenarios, where multiple tools are combined to evaluate the performance of LLMs in using various tools.

Overall, the paper provides a comprehensive overview of the evaluation methods and libraries used in assessing the performance of LLMs in handling complex tasks. It highlights the importance of evaluation datasets, metrics, and benchmarks in evaluating LLMs and suggests areas for future research and development in this field.

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