Summary of Are Large Language Models Reliable Argument Quality Annotators?, by Nailia Mirzakhmedova et al.
Are Large Language Models Reliable Argument Quality Annotators?
by Nailia Mirzakhmedova, Marcel Gohsen, Chia Hao Chang, Benno Stein
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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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 The paper investigates the potential of large language models (LLMs) to evaluate the quality of arguments in an argument mining system. The challenge lies in obtaining reliable and consistent annotations regarding argument quality, which usually requires domain-specific expertise. To address this issue, the authors analyze the agreement between LLMs, human experts, and novice annotators based on an established taxonomy of argument quality dimensions. Their findings show that LLMs can produce consistent annotations with moderate agreement with human experts across most quality dimensions. Furthermore, they demonstrate that using LLMs as additional annotators can significantly improve inter-annotator agreement. The results suggest that LLMs can serve as a valuable tool for automated argument quality assessment, streamlining and accelerating the evaluation of large argument datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well computers can evaluate arguments. Right now, it’s hard to get people to agree on how good or bad an argument is because it’s a tricky task that requires special knowledge. The authors are trying to figure out if big language models (like the ones used in chatbots) can help with this problem. They compare what these computers say about arguments with what experts and non-experts think, and they find that the computers are pretty consistent and agree with the experts most of the time. This is good news because it could make it easier to evaluate lots of arguments quickly and accurately. |
