Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs
  More Realistic and Less Risky
Authors: name: Ashutosh Hathidara, name: Julien Yu, name: Sebastian Schreiber
Organization: SAP

Large language models (LLMs) are increasingly tasked with invoking enterprise
APIs, yet they routinely falter when near-duplicate tools vie for the same user
intent or when required arguments are left underspecified. We introduce
DiaFORGE (Dialogue Framework for Organic Response Generation & Evaluation), a
disambiguation-centric, three-stage pipeline that (i) synthesizes
persona-driven, multi-turn dialogues in which the assistant must distinguish
among highly similar tools, (ii) performs supervised fine-tuning of open-source
models with reasoning traces across 3B - 70B parameters, and (iii) evaluates
real-world readiness via a dynamic suite that redeploys each model in a live
agentic loop and reports end-to-end goal completion alongside conventional
static metrics. On our dynamic benchmark DiaBENCH, models trained with DiaFORGE
raise tool-invocation success by 27 pp over GPT-4o and by 49 pp over
Claude-3.5-Sonnet, both under optimized prompting. To spur further research, we
release an open corpus of 5000 production-grade enterprise API specifications
paired with rigorously validated, disambiguation-focused dialogues, offering a
practical blueprint for building reliable, enterprise-ready tool-calling
agents.