Summary of Functionchat-bench: Comprehensive Evaluation Of Language Models’ Generative Capabilities in Korean Tool-use Dialogs, by Shinbok Lee et al.
FunctionChat-Bench: Comprehensive Evaluation of Language Models’ Generative Capabilities in Korean Tool-use Dialogs
by Shinbok Lee, Gaeun Seo, Daniel Lee, Byeongil Ko, Sunghee Jung, Myeongcheol Shin
First submitted to arxiv on: 21 Nov 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 paper explores language models’ ability to generate coherent conversations in tool-use dialogues, a crucial aspect of human-computer interaction. Researchers categorize model outputs into four types: Tool Call, Answer Completion, Slot Question, and Relevance Detection, serving as evaluation aspects. They introduce FunctionChat-Bench, a comprehensive benchmark comprising 700 items and automated assessment programs. The study evaluates various language models that support function calling, revealing that high accuracy in single-turn scenarios does not necessarily translate to superior generative performance in multi-turn environments. The findings emphasize the importance of generating conversational messages that engage users beyond just tool calls. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers looked at how well language models can have a conversation about using tools. They found different types of model outputs, like telling someone what tool to use or asking questions. To test these models, they created a big database with lots of examples and ways to evaluate them. The study showed that just being good at telling someone what tool to use doesn’t mean the model is great at having a longer conversation about using tools. |
