Summary of Evaluating Chatgpt on Nuclear Domain-specific Data, by Muhammad Anwar et al.
Evaluating ChatGPT on Nuclear Domain-Specific Data
by Muhammad Anwar, Mischa de Costa, Issam Hammad, Daniel Lau
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This abstract discusses the application of ChatGPT, a large language model (LLM), for question-and-answer (Q&A) tasks in the field of nuclear data. The study evaluates the performance of ChatGPT on a curated test dataset, comparing standalone LLM results to those generated using a Retrieval Augmented Generation (RAG) approach. The paper highlights the limitations of LLMs in generating accurate information and explores the potential of RAG to enhance output accuracy. Two methodologies are employed: direct response from the LLM and response within a RAG framework. Human and LLM evaluation mechanisms assess the responses, scoring for correctness and other metrics. The results demonstrate improved performance with RAG, particularly in generating accurate and contextually appropriate answers for nuclear domain-specific queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special language model called ChatGPT to answer questions about nuclear data. They test how well it works compared to using the model alone or combining it with other information. The problem is that these models can sometimes give wrong answers, which is not good when accuracy matters. By using more information and better searching techniques, they can get more accurate answers. They tried two ways: just letting the model answer the question and adding in extra information to help. People and computers looked at the answers and scored them for how correct they were. The results show that combining the models with other information makes it much better at giving good answers. |
Keywords
» Artificial intelligence » Language model » Large language model » Rag » Retrieval augmented generation
