Shajith Ikbal — accepted AI-conference papers

2024

Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks

Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda +20

EMNLP 2024industry

An emergent research trend explores the use of Large Language Models (LLMs) as the backbone of agentic systems (e.g., SWE-Bench, Agent-Bench). To fulfill LLMs’ potential as autonomous agents, they must be able to identify, call, and interact with a variety of external tools and application program i…

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2023

An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations

Achille Fokoue, Ibrahim Abdelaziz, Maxwell Crouse, Shajith Ikbal, Akihiro Kishimoto, Guilherme Lima +2

IJCAI 2023poster

Using reinforcement learning for automated theorem proving has recently received much attention. Current approaches use representations of logical statements that often rely on the names used in these statements and, as a result, the models are generally not transferable from one domain to another.…

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2023

Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction

Nithish Kannen, Udit Sharma, Sumit Neelam, Dinesh Khandelwal, Shajith Ikbal, Hima Karanam +1

EMNLP 2023short main

Temporal question answering (QA) is a special category of complex question answering task that requires reasoning over facts asserting time intervals of events. Previous works have predominately relied on Knowledge Base Question Answering (KBQA) for temporal QA. One of the major challenges faced by…

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2023

Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge

Shajith Ikbal, Udit Sharma, Hima Karanam, Sumit Neelam, Ronny Luss, Dheeraj Sreedhar +10

EMNLP 2023long findings

We present a neuro-symbolic approach to self-learn rules that serve as interpretable knowledge to perform relation linking in knowledge base question answering systems. These rules define natural language text predicates as a weighted mixture of knowledge base paths. The weights learned during train…

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2022

SYGMA: A System for Generalizable and Modular Question Answering Over Knowledge Bases

Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz +19

EMNLP 2022finding

Knowledge Base Question Answering (KBQA) involving complex reasoning is emerging as an important research direction. However, most KBQA systems struggle with generalizability, particularly on two dimensions: (a) across multiple knowledge bases, where existing KBQA approaches are typically tuned to a…

Cited by 24 Source ↗PDF ↗code ↗Scholar ↗

2019

Quantum Embedding of Knowledge for Reasoning

Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L Venkata Subramaniam

NeurIPS 2019poster

Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs). These methods embed any given KB into a vector space by exploiting statistical similarities among its entities and predicates but withou…

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