数据集:Aya Dataset 数据集:Bactrain-X 数据集:Baize 数据集:BELLE Generated Chat 数据集:BELLE Multiturn Chat 数据集:BELLE train 0.5M CN 数据集:BELLE train 1M CN 数据集:BELLE train 2M CN 数据集:BELLE train 3.5M CN 数据集:CAMEL 数据集:ChatGPT corpus 数据集:COIG 数据集:CrossFit 数据集:dat...
BLEU (BiLingual Evaluation Understudy, 双语评估替补)评分会根据标注的基本事实(或预期输出)评估您的 LLM 应用的输出。它会计算 LLM 输出和预期输出之间每个匹配的 n-gram(n 个连续单词)的精度,以计算它们的几何平均值,并在必要时应用简洁性惩罚。 ROUGE (Recall-Oriented Understudy for Gisting Evaluation, 面向召...
--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...
nlpbenchmarkmachine-learningleaderboardevaluationdatasetopenaillamabertragawsome-listgpt3llmawsome-listschatgptlarge-language-modelchatglmqwenllm-evaluation UpdatedOct 25, 2024 Data-Driven Evaluation for LLM-Powered Applications information-retrievalevaluation-metricsevaluation-frameworkragllmopsretrieval-augmented-gene...
A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity[J]. arXiv preprint arXiv:2302.04023, 2023.[19]Zang X, Rastogi A, Sunkara S, et al. MultiWOZ 2.2: A dialogue dataset with additional annotation corrections and state tracking baselines[J]....
“MC” indicates Model Constructed Corpus/Dataset; “CI” indicates Collection and Improvement of Existing Corpus/Dataset. Category Source Domain Instruction Category Preference Evaluation Method “VO” indicates Vote; “SO” indicates Sort; “SC” indicates Score; ...
幸运的是,这些指标的计算在langChain都已经实现,直接调用即可!https://github.com/blackinkkkxi/RAG_langchain/blob/main/learn/evaluation/RAGAS-langchian.ipynb 这有整个完整的流程参考! 先定义好prompt: 明确告知LLM根据question和context生成answer,不要自己胡乱联想,不知道就是不知道!
https://docs.aws.amazon.com/bedrock/latest/userguide/model-evaluation.html 8.DeepEval (Confident AI) 这是一个用于评估LLM的开源框架。它类似于Pytest,但专门用于单元测试LLM输出。DeepEval结合了最新的研究,根据G-Eval,幻象,答案相关性,RAGAS等指标评估LLM输出,它使用LLM和其他各种NLP模型,在您的机器上本地...
evaluation_strategy="steps",label_names=["labels"],per_device_train_batch_size=16,gradient_accumulation_steps=1,save_steps=250,eval_steps=250,logging_steps=1,learning_rate=lr,num_train_epochs=3,lr_scheduler_type="constant",gradient_checkpointing=T...
guess what it’s going to say next, and if it’s wrong, it fixes the mistake. This makes the model faster because it does not have to think as hard every single time. It is also possible to “squeeze” a better performance from LLMs with the same dataset using multi-token prediction...