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New benchmark and data-synthesis method improve LLM multi-step tool-calling

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced KOPA-Bench, a new benchmark designed to evaluate the performance of open-source LLM agents in executing multi-step tool-calls over Korean public APIs. To address the current underperformance of these models in such tasks, they developed EDGE, a data-synthesis method that dynamically graphs and verifies tool-call sequences against live APIs. Models fine-tuned using EDGE demonstrate significant improvements, with a 9B parameter model achieving performance comparable to a larger, untuned model on KOPA-Bench and the BFCL benchmark. AI

IMPACT Enhances LLM capabilities in complex, real-world API interactions, potentially improving government and enterprise automation.

RANK_REASON The cluster describes a new academic paper introducing a benchmark and a data-synthesis method for LLM tool-calling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New benchmark and data-synthesis method improve LLM multi-step tool-calling

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The cluster describes a new academic paper introducing a benchmark and a data-synthesis method for LLM tool-calling. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dain Kim, Eungi Cho, Kyumin Kim, Shinyeong Noh, Kyuseong Lim ·

    Multi-Step Tool-Calling over Korean Open Public APIs: A Benchmark and a Data-Synthesis Recipe

    arXiv:2609.05395v1 Announce Type: new Abstract: Data-sovereignty regulations increasingly require public institutions to deploy open-source, on-premise LLM agents that chain multiple tool-calls across live government APIs. However, open-source models consistently underperform in …

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