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Hima Karanam

6 accepted papers

2024

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

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…

2023

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

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…

Cited by 0SourceScholar
2023

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

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…

Cited by 0SourceScholar
2022

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

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…

2020

Inductive Quantum Embedding

NeurIPS 2020poster

Quantum logic inspired embedding (aka Quantum Embedding (QE)) of a Knowledge-Base (KB) was proposed recently by Garg:2019. It is claimed that the QE preserves the logical structure of the input KB given in the form of unary and binary predicates hierarchy. Such structure preservation allows one to p…

2019

Quantum Embedding of Knowledge for Reasoning

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…