Papers by Fahad Shah
ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering (2026.acl-long)
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| Challenge: | Large language model (LLM) agents often face strict input context limits, preventing efficient consideration of large toolsets. |
| Approach: | They propose a tool that allows LLMs to merge tools with auto-correction and toolScopeRetriever to rank and select only the most relevant tools for each query. |
| Outcome: | Evaluations on three state-of-the-art LLMs and three open-source tool-use benchmarks show gains of 8.38% to 38.6% in tool selection accuracy. |
FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Models (2025.emnlp-industry)
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Karan Dua, Hitesh Laxmichand Patel, Puneet Mittal, Ranjeet Gupta, Amit Agarwal, Praneet Pabolu, Srikant Panda, Hansa Meghwani, Graham Horwood, Fahad Shah
| Challenge: | Document understanding models require large, diverse, and well-annotated datasets that can cost millions of dollars to collect and maintain. |
| Approach: | They propose a scalable synthetic data generation framework that combines Stochastic Schemas and Parameterized Sampling to produce realistic, multilingual semi-structured documents with rich annotations. |
| Outcome: | Experiments on key information extraction tasks show that the proposed framework improves the absolute F1 score by up to 11% while reducing annotation effort by over 90% compared to traditional hard-template methods. |
JTPRO: A Joint Tool–Prompt Reflective Optimization Framework for Language Agents (2026.findings-acl)
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Sandip Ghoshal, Anshul Mittal, Jyotika Singh, Miguel Ballesteros, Weiyi Sun, Fang Tu, Shailender Singh, Yassine Benajiba, Fahad Shah, Sujeeth Bharadwaj, Sujith Ravi, Dan Roth
| Challenge: | Large language model agents struggle with ambiguous tool descriptions and underspecified tool schemas that ignore tool-specific nuances. |
| Approach: | They propose a framework for improving tool-calling reliability in trace-supervised settings by rolling out-driven reflection. |
| Outcome: | The proposed framework outperforms baselines and reflective prompt optimizers by 5%–20% on OSR. |