10 citations · 14 across the 11 of their papers we have counts for
3 papers · 1 filter
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
Ibrahim Abdelaziz, Asim Munawar, Kinjal Basu +4
Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to build, synthetic training querie…
Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs
Hussein Abdallah, Ibrahim Abdelaziz, Panos Kalnis +1
Open-world Question Answering (OW-QA) over knowledge graphs (KGs) aims to answer questions over incomplete or evolving KGs. Traditional KGQA assumes a closed world where answers mu…
Simulating Complex Multi-Turn Tool Calling Interactions in Stateless Execution Environments
Maxwell Crouse, Ibrahim Abdelaziz, Kshitij Fadnis +6
Synthetic data has proven itself to be a valuable resource for tuning smaller, cost-effective language models to handle the complexities of multi-turn tool calling conversations. W…