Summary of Afacta: Assisting the Annotation Of Factual Claim Detection with Reliable Llm Annotators, by Jingwei Ni et al.
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators
by Jingwei Ni, Minjing Shi, Dominik Stammbach, Mrinmaya Sachan, Elliott Ash, Markus Leippold
First submitted to arxiv on: 16 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 paper tackles the problem of automated fact-checking by addressing two key limitations in current methods: inconsistent definitions of factual claims and the high cost of manual annotation. The authors propose a unified definition of factual claims that focuses on verifiability, and introduce AFaCTA (Automatic Factual Claim deTection Annotator), a framework that uses large language models to assist in annotating factual claims. AFaCTA calibrates its annotation confidence along three predefined reasoning paths, allowing it to efficiently annotate claims with high-quality classifiers, both with or without expert supervision. The authors also introduce PoliClaim, a comprehensive claim detection dataset covering diverse political topics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn to tell true from false statements on the internet. It solves two big problems: people not agreeing on what a “fact” is, and manual checking being too time-consuming and expensive. To fix this, the researchers define what a fact is in a way that makes sense, and create a tool called AFaCTA that uses special language models to help experts label facts correctly. This tool can work with or without human supervision, making it useful for training computers to detect fake news. |
