Papers by Sebastian Schreiber
Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly tasked with invoking enterprise APIs . however, they falter when near-duplicate tools vie for the same user intent . cnn's john mccartney and johnny mccain present a disambiguation-centric pipeline . |
| Approach: | They propose a disambiguation-centric pipeline that synthesizes persona-driven dialogues . they use a corpus of API specifications and rigorously validated dialogues to build reliable tools . |
| Outcome: | The proposed pipeline raises tool-invocation success by 27 pp over GPT-4o and 49 pp above Claude-3.5-Sonnet on a dynamic benchmark. |