Summary of Dialogue Action Tokens: Steering Language Models in Goal-directed Dialogue with a Multi-turn Planner, by Kenneth Li et al.
Dialogue Action Tokens: Steering Language Models in Goal-Directed Dialogue with a Multi-Turn Planner
by Kenneth Li, Yiming Wang, Fernanda Viégas, Martin Wattenberg
First submitted to arxiv on: 17 Jun 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 A novel Dialogue Action Tokens (DAT) approach is proposed to adapt language model agents for planning goal-directed dialogues by treating each utterance as an action. This game-like framework leverages reinforcement learning techniques to predict continuous action vectors for controlled generation in each round, addressing the issue of language degradation under reward optimization. The DAT-steered LLaMA model outperforms GPT-4 on the Sotopia platform for social simulations, and is also applied to steer an attacker language model in a novel multi-turn red-teaming setting, revealing potential new attack surfaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dialogue Action Tokens (DAT) is a way to make language models better at planning conversations. It works by treating each thing someone says as a move in a game, which helps the model learn what actions to take next. This approach was tested on a platform that simulates social situations and found to be more effective than another popular model, GPT-4. DAT can also be used to make an attacker model more sophisticated, revealing new ways that language models could be used maliciously. |
Keywords
» Artificial intelligence » Gpt » Language model » Llama » Optimization » Reinforcement learning
