Summary of Interactive Dialogue Agents Via Reinforcement Learning on Hindsight Regenerations, by Joey Hong et al.
Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations
by Joey Hong, Jessica Lin, Anca Dragan, Sergey Levine
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 In this paper, researchers focus on improving large language models (LLMs) to generate text that can steer conversations effectively. Current LLMs excel at responding to questions and requests with a single accurate response. However, in real-world dialogues, an agent’s utterances often influence their conversational partner, eliciting information or changing opinions. To achieve this ability, existing methods require expert data curation, which relies on understanding cognitive processes that are challenging for humans and LLMs. Instead, the authors propose using post-hoc rewriting and augmentation of suboptimal data to train an agent via offline reinforcement learning (RL). This approach outperforms prompting and learning from unaltered human demonstrations in two domains: mental health support and soliciting charitable donations. The results show that the proposed approach significantly outperforms state-of-the-art dialogue agents in a user study with real humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make computers better at having conversations with people. They’re looking at how big language models can talk to each other and influence what the other person says. This is important because it’s like when you have a conversation with someone and they change their mind or share new information. The problem is that current computer systems aren’t very good at this. They just give one answer and then stop talking. The researchers think they can do better by taking small steps back and looking at what happened in the conversation, then using that to learn how to improve the next time. They tested their idea on two topics: helping people with mental health issues and asking for donations for a good cause. Their results show that their way of doing things works much better than existing computer systems. |
Keywords
» Artificial intelligence » Prompting » Reinforcement learning
