Summary of Towards Faithful and Robust Llm Specialists For Evidence-based Question-answering, by Tobias Schimanski et al.
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
by Tobias Schimanski, Jingwei Ni, Mathias Kraus, Elliott Ash, Markus Leippold
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper tackles the issue of Large Language Models (LLMs) providing faithful and traceable answers, a crucial step for various research and practical applications. The authors investigate ways to improve source quality and answer attributability by fine-tuning LLMs using reliable sources. They introduce a data generation pipeline with automated filters to synthesize high-quality training and testing data at scale. This approach improves performance on both in- and out-of-distribution test sets, demonstrating the effectiveness of fine-tuning specialist models for Evidence-Based QA. The study highlights the importance of data quality over quantity in enhancing the accuracy of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to get accurate answers from a super smart computer program called Large Language Models (LLMs). These programs are great at answering questions, but they often don’t give credit where credit is due. The authors of this paper want to make sure the answers are trustworthy and based on good sources. They developed a way to train these models using lots of high-quality data, which makes them better at finding accurate information. This is important because it helps us get more reliable answers from these powerful machines. |
Keywords
* Artificial intelligence * Fine tuning
