Summary of Role-play Zero-shot Prompting with Large Language Models For Open-domain Human-machine Conversation, by Ahmed Njifenjou et al.
Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain Human-Machine Conversation
by Ahmed Njifenjou, Virgile Sucal, Bassam Jabaian, Fabrice Lefèvre
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 paper proposes a novel approach to creating open-domain conversational agents using Large Language Models (LLMs). Currently, LLMs can answer user queries, but only in a one-way Q&A format. To improve their conversational ability, fine-tuning on specific datasets is often used, but this is costly and typically limited to a few languages. The authors explore role-play zero-shot prompting as an efficient and cost-effective solution for open-domain conversation, leveraging capable multilingual LLMs trained to obey instructions. They design a prompting system that, when combined with the Vicuna model, produces conversational agents that match or even surpass fine-tuned models in human evaluation, specifically in French, across two tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating machines that can have conversations like humans do. Right now, computers can answer questions, but it’s not a real conversation. To make them better at talking, people usually need to train the computer on lots of data, which is expensive and only works for a few languages. The researchers in this study found a way to get computers to talk more naturally without needing all that training data. They used special instructions and a model called Vicuna to create conversational agents that are as good as or even better than ones trained on lots of data. |
Keywords
* Artificial intelligence * Fine tuning * Prompting * Zero shot
