Summary of Gta: a Benchmark For General Tool Agents, by Jize Wang et al.
GTA: A Benchmark for General Tool Agents
by Jize Wang, Zerun Ma, Yining Li, Songyang Zhang, Cailian Chen, Kai Chen, Xinyi Le
First submitted to arxiv on: 11 Jul 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 research paper proposes a new benchmark, GTA, to evaluate the tool-use capabilities of large language models (LLMs) in real-world scenarios. The existing evaluations often use artificial queries, single-step tasks, and text-only interactions, which do not accurately reflect the challenges that LLMs face when working with various tools. The proposed GTA benchmark addresses these limitations by featuring three main aspects: real user queries, real deployed tools, and real multimodal inputs. The benchmark includes 229 real-world tasks and executable tool chains to evaluate mainstream LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about testing how well language models can work with different tools to solve problems in the real world. Current tests are too easy because they don’t use real user queries or tools that we actually use every day. The researchers created a new test called GTA that uses real-world scenarios and problems to see if language models like GPT-4 can really help us. The results show that current language models struggle with this kind of work, which is important to know so we can make them better. |
Keywords
» Artificial intelligence » Gpt
