5 papers
ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs
Ashutosh Hathidara, Sai Shruthi Sistla, Sebastian Schreiber +1
Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck. As embedding-based retrieval approaches rely on compact encoders that m…
CoHyDE: Iterative Co-Training of LLM Rewriter & Dense Encoder for Tool Retrieval
Vaishali Senthil, Ashutosh Hathidara, Sebastian Schreiber
Tool retrieval over large API catalogs is a core bottleneck for LLM agents: user queries arrive in colloquial, often underspecified language, while the catalog uses technical API v…
MirrorBench: A Benchmark to Evaluate Conversational User-Proxy Agents for Human-Likeness
Ashutosh Hathidara, Julien Yu, Vaishali Senthil +2
Large language models (LLMs) are increasingly used as human simulators, both for evaluating conversational systems and for generating fine-tuning data. However, naive "act-as-a-use…
Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky
Ashutosh Hathidara, Julien Yu, Sebastian Schreiber
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 require…
Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning
Sahil Bansal, Sai Shruthi Sistla, Aarti Arikatala +1
Effective tool pre-selection via retrieval is essential for AI agents to select from a vast array of tools when identifying and planning actions in the context of complex user quer…