Summary of An Audit on the Perspectives and Challenges Of Hallucinations in Nlp, by Pranav Narayanan Venkit et al.
An Audit on the Perspectives and Challenges of Hallucinations in NLP
by Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta, Heidi Biggs, Mukund Srinath, Koustava Goswami, Sarah Rajtmajer, Shomir Wilson
First submitted to arxiv on: 11 Apr 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 paper examines how large language models (LLMs) are described as “hallucinating” in 103 peer-reviewed publications across natural language processing (NLP) research. The authors find that there is no consensus on what constitutes “hallucination” in this field, leading to a call for explicit definitions and frameworks to standardize the term. To complement their analysis, the researchers surveyed 171 NLP and AI practitioners to gather perspectives on hallucination. Their findings highlight potential challenges associated with the phenomenon of hallucination in LLMs and its societal implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how scientists describe something called “hallucination” in big language models. They found that different researchers don’t agree on what it means for a model to “hallucinate”. To understand this better, they asked 171 experts in the field what they think about hallucination. The results show that we need clear rules and definitions to talk about this topic, and it could have big effects on society. |
Keywords
» Artificial intelligence » Hallucination » Natural language processing » Nlp
