Summary of Enhancing Large Language Model Performance to Answer Questions and Extract Information More Accurately, by Liang Zhang et al.
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately
by Liang Zhang, Katherine Jijo, Spurthi Setty, Eden Chung, Fatima Javid, Natan Vidra, Tommy Clifford
First submitted to arxiv on: 27 Jan 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 research paper explores ways to improve the performance of Large Language Models (LLMs) in generating accurate answers to questions. The current limitations of LLMs include suboptimal answer quality and occasional failures. To address these issues, the authors propose a fine-tuning process that incorporates feedback and examples to refine models. This approach utilizes metrics such as cosine similarity, LLM evaluation, and Rouge-L scores to evaluate model performance. The study benchmarks this method on financial datasets, including FinanceBench and RAG Instruct Benchmark Tester Dataset, demonstrating the importance of fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making AI language models better at answering questions. Right now, these models can sometimes give wrong answers or not answer questions at all. To fix this, researchers are trying a new way to train the models by giving them feedback and examples. This helps make the models more accurate and good at answering questions. The study looks at how well this method works on financial data and shows that it makes a big difference. |
Keywords
» Artificial intelligence » Cosine similarity » Fine tuning » Rag » Rouge
