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New SLATE benchmark reveals challenges in enterprise LLM API selection

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SLATE, a new benchmark designed to evaluate the ability of large language model agents to select and compose APIs for complex, long-horizon tasks in an e-commerce setting. Unlike existing benchmarks, SLATE utilizes a library of approximately 1,000 APIs and a simulator that measures end-to-end task success automatically and reproducibly. Experiments with models ranging from 3B to 12B parameters revealed that even the most advanced models achieved only a 5.0% success rate, highlighting the significant challenge in reliable enterprise tool use. AI

IMPACT Highlights significant limitations in current LLM agents for complex enterprise automation and tool use.

RANK_REASON The cluster describes a new academic benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SLATE benchmark reveals challenges in enterprise LLM API selection

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The cluster describes a new academic benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    SLATE: A Large-Scale Benchmark for Long-Horizon API Selection in E-commerce Tool Use

    Enterprises increasingly want LLM agents to automate standard operating procedures over their software stacks -- mapping each step of a procedure to the right call among thousands of internal APIs, without hand-authored step-to-API mappings. The core difficulty is API selection a…

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