Summary of Aqua — Combining Experts’ and Non-experts’ Views to Assess Deliberation Quality in Online Discussions Using Llms, by Maike Behrendt et al.
AQuA – Combining Experts’ and Non-Experts’ Views To Assess Deliberation Quality in Online Discussions Using LLMs
by Maike Behrendt, Stefan Sylvius Wagner, Marc Ziegele, Lena Wilms, Anke Stoll, Dominique Heinbach, Stefan Harmeling
First submitted to arxiv on: 3 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 The proposed paper introduces AQuA, an additive score that calculates a unified deliberative quality score from multiple indices for each discussion post. This comprehensive score incorporates various deliberative aspects, enhancing model transparency and aligning well with annotations on other datasets. The authors develop adapter models for 20 deliberative indices and demonstrate how the AQuA score can be computed easily from pre-trained adapters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to measure online discussions is being developed. This system, called AQuA, looks at many different things in a conversation to figure out how good it is. It’s like a report card for online talks. The researchers made special tools that help calculate this score and tested them on lots of conversations. They found that their way of scoring works well with other ways people have tried to rate discussions. This could be useful for understanding what makes an online conversation good or bad. |
