Summary of Diverse and Fine-grained Instruction-following Ability Exploration with Synthetic Data, by Zihui Gu et al.
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data
by Zihui Gu, Xingwu Sun, Fengzong Lian, Zhanhui Kang, Cheng-Zhong Xu, Ju Fan
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 This paper proposes a novel approach to evaluating the instruction-following capabilities of large language models (LLMs). The existing methods focus on general skills and lack fine-grained task-level evaluation, which makes it challenging to assess the performance of LLMs in real-world scenarios. To address this issue, the authors introduce DINGO, a dataset that is based on a manually annotated, multi-level category tree with 130 nodes derived from real-world user requests. DINGO includes diverse instructions generated by both GPT-4 and human experts, making it a comprehensive evaluation tool for LLMs. The authors demonstrate through extensive experiments that DINGO provides more challenging and comprehensive evaluation for LLMs, allowing task-level fine-grained directions to further improve their performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping computers understand and follow instructions better. Right now, there are problems with how we evaluate these computer abilities because our current methods don’t take into account the complexity of real-world user requests. The authors created a new dataset called DINGO that can help solve this problem. DINGO is based on categories that humans use to make sense of things, and it includes many different types of instructions generated by both computers and humans. By using DINGO, we can test computer abilities more accurately and give them better feedback to improve their performance. |
Keywords
» Artificial intelligence » Gpt
