22nd August 2024 Controllable Text Generation for Large Language Models: A Survey In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly complex requirements. Beyond avoiding mis...
RAG (Retrieval-Augmented Generation) : Integrates the retrieval (searching) into LLM text generation. RAG helps the model to “look up” external information to improve its responses.cite[25 Aug 2023] In a 2020 paper, Meta (Facebook) came up with a framework called retrieval-augmented generatio...
Or, if you are using an LLM for translation between two languages, you can query the evaluator LLM with the original text and the LLM-provided translation, asking if that translation is correct. Relative task difficulty How is this possible, I hear you asking. How can a model evaluate itsel...
Figure 1. Selection of Studies in Systematic Review of the Testing and Evaluation of Large Language Models (LLMs) View LargeDownload Figure 2. Heat Map of Health Care Tasks, Natural Language Processing (NLP) and Natural Language Understanding (NLU) Tasks, and Dimensions of Evaluation Across 519...
In contrast to the above mentioned works on evaluating the truthfulness of LLMs, which usually use the widely recognized powerful LLMs such as GPT-4 and ChatGPT as the evaluator to judge the LLMs’ truthfulness, with the LLMs used for generating the text usually being different from the ...
The new approach trains LLMs to create their own training data for evaluation purposes. Credit: Shutterstock Facebook parent Meta’s AI research team is working on developing what it calls a Self-Taught Evaluator for large language models (LLMs) that could help enterprises reduce their time ...
首先,研究者使用 GPT-4生成了一个包含38个主题、数千个问题的提示集。然后提出了搜索增强事实评估器(Search-Augmented Factuality Evaluator,SAFE)来将LLM智能体用作长篇事实性的自动评估器。实证结果表明,LLM 智能体可以实现超越人类的评级性能。同时,SAFE 的成本比人类注释者便宜20倍以上。
Is my Meeting Summary Good? Estimating Quality with a Multi-LLM Evaluator 来自 arXiv.org 喜欢 0 阅读量: 1 作者:Kirstein, Frederic,Ruas, Terry,Gipp, Bela 摘要: The quality of meeting summaries generated by natural language generation (NLG) systems is hard to measure automatically. Established ...
The actual duration of an evaluation depends on the size of the prompt dataset and on the generator and the evaluator models used. At the top, the Metric summary evaluates the overall performance using the average score across all conversations. After that, the Generation metrics breakdown give...
Ragas- a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. LLM Training Frameworks Reference:llm-inference-solutions Miscellaneous Contributing This is an active repository and your contributions are always welcome!