SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientifc Research SciEval 是一个基于科学原则设计的多层次评估基准,结合静态和动态数据,全面评估大型语言模型在基础知识、知识应用、科学计算和研究能力四个维度的科学研究能力。 SciEval: A Multi-Level Large Language Model Evaluation Ben...
model_name = "lamini/lamini_docs_finetuned" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) 1. 2. 3. 2.4 设置基础评估函数 def is_exact_match(a, b): return a.strip() == b.strip() model.eval() 1. 2. 3. 4. 输出如...
1. 模型训练 (Pretrain) 2. 模型适配(Adaption) 3. 模型使用(Utilization) 4. 模型评估(Evaluation) 此文高度总结LLM,并把LLM综述文章里提到的常用技术部分展开介绍。 背景(什么是LLM Large language Model) 一句话:超大规模训练数据量训练出来的超大规模参数量的模型,模型的能力也由量变上升到质变。量变...
large language models evaluation Language models have become increasingly advanced and sophisticated in recent years, with larger models such as GPT-3 gaining attention for their ability to generate coherent and contextually relevant text. These largelanguage models have the potential to revolutionize ...
(2) Evaluation API-Bank[69]是一个用于评估工具增强型LLM性能的测试集。它包含53个常用的API工具、完整的工具增强型LLM工作流程以及264个涉及568个API调用的带注释的对话。API的选择非常多样化,包括搜索引擎、计算器、日历查询、智能家居控制、日程管理、健康数据管理、帐户验证工作流程等。由于API数量众多,LLM首先可以...
0x2:AI Model Evaluation AI模型评估是评估模型性能的关键步骤。 有一些标准的模型评估协议,包括: k折交叉验证(k-fold cross-validation):k折交叉验证将数据集分为k个部分,其中一个部分用作测试集,其余部分用作训练集,可以减少训练数据损失并获得相对更准确的模型性能评估。
https://research.aimultiple.com/large-language-model-evaluation/https://mp.weixin.qq.com/s/FeAH_30IkXHNfywKXoog1whttps://github.com/llmeval/llmeval-1 回到顶部(go to top) 三、high-quality preferences dateset prepare,偏好数据集准备
For each candidate answer, we first calculate the perplexity (PPL) of the model individually and select the candidate with the lowest perplexity as the final choice. To ensure a fair evaluation, we primarily rely on two open-source evaluation frameworks, lm-evaluation-harness8 and OpenCompass.9...
Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks, yet their evaluation, particularly in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations. Employing LLMs as evaluators to...
Other: 如电商领域、Few-shot learning领域、地球科学领域、IT领域、多轮交互领域、鲁棒性、语义领域等。 4.2 Evaluation Method 评估方法被分位三个类别:code evaluation、human evaluation、model evaluation。 4.3 Evaluation Dataset Summary