GTA: A Benchmark for General Tool Agents
Significant focus has been placed on integrating large language models (LLMs)
with various tools in developing general-purpose agents. This poses a challenge
to LLMs' tool-use capabilities. However, there are evident gaps between
existing tool-use evaluations and real-world scenarios. Current evaluations
often use AI-generated queries, single-step tasks, dummy tools, and text-only
interactions, failing to reveal the agents' real-world problem-solving
abilities effectively. To address this, we propose GTA, a benchmark for General
Tool Agents, featuring three main aspects: (i) Real user queries: human-written
queries with simple real-world objectives but implicit tool-use, requiring the
LLM to reason the suitable tools and plan the solution steps. (ii) Real
deployed tools: an evaluation platform equipped with tools across perception,
operation, logic, and creativity categories to evaluate the agents' actual task
execution performance. (iii) Real multimodal inputs: authentic image files,
such as spatial scenes, web page screenshots, tables, code snippets, and
printed/handwritten materials, used as the query contexts to align with
real-world scenarios closely. We design 229 real-world tasks and executable
tool chains to evaluate mainstream LLMs. Our findings show that real-world user
queries are challenging for existing LLMs, with GPT-4 completing less than 50%
of the tasks and most LLMs achieving below 25%. This evaluation reveals the
bottlenecks in the tool-use capabilities of current LLMs in real-world
scenarios, which provides future direction for advancing general-purpose tool
agents. The code and dataset are available at
https://github.com/open-compass/GTA.
66909d0698c0444e3a7e408a
Jize Wang
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2024-08-13T07:39:49.961Z
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Jize
user
Jize1
66909d0698c0444e3a7e408b
Zerun Ma
claimed_verified
2024-07-22T08:43:33.636Z
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mzr1996
user
mzr1996
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Yining Li
admin_assigned
2024-07-12T09:58:52.289Z
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Yining Li
user
ly015
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Songyang Zhang
admin_assigned
2024-07-12T09:57:07.530Z
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Songyang Zhang
user
zsytony
66909d0698c0444e3a7e408e
Cailian Chen
66909d0698c0444e3a7e408f
Kai Chen
66909d0698c0444e3a7e4090
Xinyi Le
GTA: A Benchmark for General Tool Agents
Significant focus has been placed on integrating large language models (LLMs)
with various tools in developing general-purpose agents. This poses a challenge
to LLMs' tool-use capabilities. However, there are evident gaps between
existing tool-use evaluations and real-world scenarios. Current evaluations
often use AI-generated queries, single-step tasks, dummy tools, and text-only
interactions, failing to reveal the agents' real-world problem-solving
abilities effectively. To address this, we propose GTA, a benchmark for General
Tool Agents, featuring three main aspects: (i) Real user queries: human-written
queries with simple real-world objectives but implicit tool-use, requiring the
LLM to reason the suitable tools and plan the solution steps. (ii) Real
deployed tools: an evaluation platform equipped with tools across perception,
operation, logic, and creativity categories to evaluate the agents' actual task
execution performance. (iii) Real multimodal inputs: authentic image files,
such as spatial scenes, web page screenshots, tables, code snippets, and
printed/handwritten materials, used as the query contexts to align with
real-world scenarios closely. We design 229 real-world tasks and executable
tool chains to evaluate mainstream LLMs. Our findings show that real-world user
queries are challenging for existing LLMs, with GPT-4 completing less than 50%
of the tasks and most LLMs achieving below 25%. This evaluation reveals the
bottlenecks in the tool-use capabilities of current LLMs in real-world
scenarios, which provides future direction for advancing general-purpose tool
agents. The code and dataset are available at
https://github.com/open-compass/GTA.
large language models (LLMs) general-purpose agents tool-use capabilities tool-use evaluations AI-generated queries single-step tasks dummy tools real user queries implicit tool-use suitable tools solution steps real deployed tools evaluation platform perception operation logic creativity real multimodal inputs spatial scenes web page screenshots tables code snippets printed/handwritten materials query contexts real-world tasks executable tool chains mainstream LLMs GPT-4