Summary of Intent-conditioned and Non-toxic Counterspeech Generation Using Multi-task Instruction Tuning with Rlaif, by Amey Hengle et al.
Intent-conditioned and Non-toxic Counterspeech Generation using Multi-Task Instruction Tuning with RLAIF
by Amey Hengle, Aswini Kumar, Sahajpreet Singh, Anil Bandhakavi, Md Shad Akhtar, Tanmoy Chakroborty
First submitted to arxiv on: 15 Mar 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 The proposed framework, CoARL, enhances counterspeech generation by modeling pragmatic implications underlying social biases in hateful statements. It comprises three phases: sequential multi-instruction tuning to understand intents and reactions of offensive statements, learning task-specific low-rank adapter weights for generating intent-conditioned counterspeech, and fine-tuning outputs using reinforcement learning. This approach outperforms existing benchmarks in intent-conditioned counterspeech generation, achieving an average improvement of 3 points in intent-conformity and 4 points in argument-quality metrics. The framework’s efficacy is further supported by extensive human evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoARL is a new way to create responses that counteract online hate speech. This approach helps machines understand the meaning behind hateful statements, generating responses that address the biases and stereotypes present. By fine-tuning its outputs for effectiveness and non-toxicity, CoARL creates better counterspeech than existing systems like ChatGPT. The results show that CoARL is more effective in generating context-appropriate and superior responses. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Reinforcement learning
