Summary of Northeastern Uni at Multilingual Counterspeech Generation: Enhancing Counter Speech Generation with Llm Alignment Through Direct Preference Optimization, by Sahil Wadhwa et al.
Northeastern Uni at Multilingual Counterspeech Generation: Enhancing Counter Speech Generation with LLM Alignment through Direct Preference Optimization
by Sahil Wadhwa, Chengtian Xu, Haoming Chen, Aakash Mahalingam, Akankshya Kar, Divya Chaudhary
First submitted to arxiv on: 19 Dec 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 proposes a novel methodology for generating high-quality counter-speech (CS) to address hate speech by leveraging Large Language Models (LLMs). The approach combines Supervised Fine-Tuning (SFT) with Direct Preference Optimization (DPO) to align LLM outputs with human preferences, ensuring contextually appropriate and linguistically adaptable responses. The method also incorporates knowledge grounding to enhance factual accuracy and relevance of generated CS. Experimental results show that DPO-aligned models outperform SFT baselines on CS benchmarks while scaling effectively to multiple languages, including Basque, Italian, and Spanish. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to help fight hate speech by creating a special kind of response called counter-speech (CS). Existing methods for generating CS aren’t very good at making high-quality responses that work well across different languages. The authors of this paper propose a new way to improve CS generation using large language models and human preferences. They test their method on several languages, including Basque, Italian, and Spanish, and show that it produces better results than other methods. This research could help create more effective ways to respond to hate speech in different languages. |
Keywords
» Artificial intelligence » Fine tuning » Grounding » Optimization » Supervised
