Summary of Llm-based Frameworks For Api Argument Filling in Task-oriented Conversational Systems, by Jisoo Mok et al.
LLM-based Frameworks for API Argument Filling in Task-Oriented Conversational Systems
by Jisoo Mok, Mohammad Kachuee, Shuyang Dai, Shayan Ray, Tara Taghavi, Sungroh Yoon
First submitted to arxiv on: 27 Jun 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 explores the application of Large Language Models (LLMs) for the task-oriented conversational agent’s API argument filling task. The authors identify that LLMs require an additional grounding process to successfully perform this task, which involves providing arguments required by the selected API. To address this limitation, the researchers design training and prompting frameworks to ground the responses of LLMs. Experimental results demonstrate that when paired with these proposed techniques, the argument filling performance of LLMs noticeably improves, opening up new possibilities for building automated argument filling frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a special kind of computer program called a conversational agent. It helps people by interacting with them and providing information. There are three main steps to make this happen: choosing an external API, filling in the right arguments, and generating a response. The authors found that these agents need help understanding what they should say next, so they used big language models (LLMs) to try and solve this problem. They discovered that LLMs also need some extra guidance to do their job well, but when they got it, the results were much better! This means we might be able to build more helpful computer programs in the future. |
Keywords
* Artificial intelligence * Grounding * Prompting
