Tool use / function calling

StableToolBench

StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models

Superseded baseline#18 of 55 most-superseded · first seen Mar 12, 2024

Cited as a baseline — critiqued by newer work, not yet beaten on a benchmark here

2 papers critique it · 0 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites StableToolBench as a baseline.

Most public benchmarks still overlook other enterprise-grade challenges, notably distinguishing among near-duplicate tools, proactively eliciting mandatory arguments, and detecting or preventing tool-call hallucinations, shortcomings our framework is expressly designed to remedy.
Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky
StableToolBench emphasized stability and reproducibility through API simulation and caching mechanisms, highlighting evaluation brittleness in real API-dependent setups
From Language to Action in Arabic: Reliable Structured Tool Calling via Data-Centric Fine-Tuning

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.