10 citations · 11 across the 8 of their papers we have counts for
5 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…
Chatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering over Knowledge Graphs
Reham Omar, Abdelghny Orogat, Ibrahim Abdelaziz +3
Conversational Question Answering over Knowledge Graphs (KGs) combines the factual grounding of KG-based QA with the interactive nature of dialogue systems. KGs are widely used in…
ToolRM: Outcome Reward Models for Tool-Calling Large Language Models
Mayank Agarwal, Ibrahim Abdelaziz, Kinjal Basu +4
As large language models (LLMs) increasingly interact with external tools, reward modeling for tool use has emerged as a critical yet underexplored area of research. Existing rewar…