Summary of Towards a Holistic Evaluation Of Llms on Factual Knowledge Recall, by Jiaqing Yuan et al.
Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall
by Jiaqing Yuan, Lin Pan, Chung-Wei Hang, Jiang Guo, Jiarong Jiang, Bonan Min, Patrick Ng, Zhiguo Wang
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 focuses on evaluating the factuality of Large Language Models’ (LLMs) generated outputs, particularly in regards to tackling hallucinations that still plague these models. The authors examine how LLMs perform on various Natural Language Processing (NLP) tasks and explore their applications in diverse use cases. By holistically assessing the veracity of LLM-generated text, this research aims to shed light on the capabilities and limitations of these powerful AI tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can do lots of things with words! They’re really good at understanding human language, but sometimes they make mistakes. This paper tries to figure out if what they say is true or not. It’s important because we want to use these computers for all sorts of tasks, like helping us learn new things or translating languages. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
