Models are benchmarked based on their capabilities, such as coding, common sense and reasoning. Other capabilities encompass natural language processing, including machine translation, question answering and text summarization. LLM benchmarks play a crucial role in developing and enhancing models. Benchma...
--dataset-args: 数据集的evaluation settings,以json格式传入,key为数据集名称,value为参数,注意需要跟--datasets参数中的值一一对应 --few_shot_num: few-shot的数量 --few_shot_random: 是否随机采样few-shot数据,如果不设置,则默认为true --limit: 每个subset最大评估数据量 --template-type: 需要手动指定...
裁判员模型(e.g. GPT-4、Claude、Expert Models/Reward models) LLM Peer-examination 如何评估一个LLM 哪些维度? 语义理解(Understanding) 知识推理(Reasoning) 专业能力(e.g. coding、math) 应用能力(MedicalApps、AgentApps、AI-FOR-SCI ...) 指令跟随(Instruction Following) 鲁棒性(Robustness) 偏见(Bias) ...
支持输入多个数据集,使用空格分开,参考下文数据集列表章节- --use-cache: 是否使用本地缓存,默认为false;如果为true,则已经评估过的模型和数据集组合将不会再次评估,直接从本地缓存读取 - --dataset-args: 数据集的evaluation settings,以json格式传入,key为数据集名称,value为参数,注意需要跟--datasets参数中...
大型语言模型评估(LLMs evaluation)已成为评价和改进大模型的重要流程和手段,为了更好地支持大模型的评测,我们提出了llmuses框架,该框架主要包括以下几个部分: 预置了多个常用的测试基准数据集,包括:MMLU、CMMLU、C-Eval、GSM8K、ARC、HellaSwag、TruthfulQA、MATH、HumanEval等 常用评估指标(metrics)的实现 统一m...
裁判员模型(e.g. GPT-4、Claude、Expert Models/Reward models) LLM Peer-examination 如何评估一个LLM 哪些维度? 语义理解(Understanding) 知识推理(Reasoning) 专业能力(e.g. coding、math) 应用能力(MedicalApps、AgentApps、AI-FOR-SCI ...) 指令跟随(Instruction Following) ...
专业能力(e.g. coding、math) 应用能力(MedicalApps、AgentApps、AI-FOR-SCI …) 指令跟随(Instruction Following) 鲁棒性(Robustness) 偏见(Bias) 幻觉(Hallucinations) 安全性(Safety) 例:GPT-4 vs LLaMA2-7B能力维度对比评测 1. 自动评估方法 模型效果评估 基准和指标(Benchmarks & Metrics) 数...
2.Benchmarking the text-to-sql capability of large language models: A comprehensive evaluation。从5...
Figure 1. StarCoder2 15B delivers superior accuracy on HumanEval benchmark With a context length of 16,000 tokens, Starcoder models can handle a longer code base and elaborate coding instructions, get a better understanding of code structure, and provide improved code documentation. ...
code-davinci-002+ SFT ->text-davinci-002: SFT on stronger base model shows the opposite effect, which underperforms the base model on most benchmarks (except for coding tasks, which is further discussed below). A similar trend is observed on open-source models: SFT boosts the performance...