Summary of Bootstrapping Llm-based Task-oriented Dialogue Agents Via Self-talk, by Dennis Ulmer et al.
Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk
by Dennis Ulmer, Elman Mansimov, Kaixiang Lin, Justin Sun, Xibin Gao, Yi Zhang
First submitted to arxiv on: 10 Jan 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 method utilizes large language models (LLMs) engaging in a conversation in various roles to generate training data via “self-talk”. This approach refines and utilizes the LLMs for supervised fine-tuning. The method also introduces an automated way to measure the success of a dialogue, filtering generated conversational data that is fed back into the LLM for training. The results demonstrate that such self-talk data improves conversation quality, as shown by both automated and human evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very smart computers that can have conversations with humans. But it’s hard to make them do specific tasks, like following a certain workflow in a conversation. To solve this problem, researchers came up with a new way to train these models using a technique called “self-talk”. This involves the model having a conversation with itself, taking on different roles and generating responses. The good conversations are then used to fine-tune the model, making it better at following instructions. |
Keywords
» Artificial intelligence » Fine tuning » Supervised
