Summary of A Multi-aspect Framework For Counter Narrative Evaluation Using Large Language Models, by Jaylen Jones et al.
A Multi-Aspect Framework for Counter Narrative Evaluation using Large Language Models
by Jaylen Jones, Lingbo Mo, Eric Fosler-Lussier, Huan Sun
First submitted to arxiv on: 18 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 Machine learning models have emerged as effective tools in combating hate speech. Automatic methods can aid manual interventions by generating counter-narratives that refute hateful claims and de-escalate encounters. However, the evaluation of these approaches remains underdeveloped due to limitations in existing metrics. These metrics rely on superficial reference comparisons rather than incorporating key aspects of counter-narrative quality as evaluation criteria. To address these limitations, we propose a novel evaluation framework that prompts language models (LLMs) to provide scores and feedback for generated counter-narrative candidates using 5 defined aspects derived from guidelines from specialized NGOs. Our results show that LLM evaluators achieve strong alignment with human-annotated scores and feedback, outperforming alternative metrics. This suggests their potential as multi-aspect, reference-free, and interpretable evaluators for counter-narrative evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hate speech is a big problem, and scientists are working on ways to stop it. One way is by creating responses that show people the error of their hateful ways. Computers can help with this by making these responses automatically. But we need a good way to check if these computer-made responses are doing a good job. Right now, we don’t have a great system for checking them. So, we came up with a new way to do it. We had computers look at the responses and give feedback on how well they’re doing. It turns out that our new system is really good at catching what’s important in these responses, which helps us stop hate speech. |
Keywords
» Artificial intelligence » Alignment » Machine learning
