Summary of Factuality Of Large Language Models: a Survey, by Yuxia Wang et al.
Factuality of Large Language Models: A Survey
by Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Georgiev, Rocktim Jyoti Das, Preslav Nakov
First submitted to arxiv on: 4 Feb 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 survey aims to critically analyze existing work on evaluating and improving the factuality of large language models (LLMs), particularly in scenarios where they provide single-source answers to a variety of questions. Despite their widespread adoption, LLMs often produce factually incorrect responses, limiting their applicability. The paper identifies major challenges, their causes, and potential solutions for improving factuality, while also discussing obstacles to automated evaluation for open-ended text generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are everywhere! They can answer questions in one place, making life easier. But sometimes they get facts wrong, which is a big problem. Researchers are working hard to make these models more accurate. This survey looks at what’s been done so far and finds the biggest challenges and why they happen. It also suggests ways to fix these issues and talks about how we can automatically check if answers are correct. |
Keywords
» Artificial intelligence » Text generation
