Papers by Chaitanya Agarwal
Hybrid Graphs for Table-and-Text based Question Answering using LLMs (2025.naacl-long)
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| Challenge: | Current methods for QA rely on fine-tuning and high-quality data, which is difficult to obtain. |
| Approach: | They propose a Hybrid Graph-based approach for Table-Text QA that leverages Large Language Models without fine-tuning. |
| Outcome: | The proposed approach improves Exact Match scores by 10% on Hybrid-QA and 5.4% on OTT-QA. |
Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments (2025.emnlp-main)
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| Challenge: | Enterprise systems are crucial for enhancing productivity and strategic growth, but data is fragmented across multiple sources and access controls are complex. |
| Approach: | They propose a benchmark that simulates enterprise settings with 500 diverse tasks . they show that even the most capable models achieve only 41.8% task completion . |
| Outcome: | The proposed benchmark shows that even the most capable models achieve only 41.8% task completion. |
HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMs (2024.acl-long)
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| Challenge: | Existing approaches to answer multi-hop questions are query-agnostic and the extracted facts are ambiguous as they lack context. |
| Approach: | They propose to use a knowledge graph to extract query-relevant information from unstructured text. |
| Outcome: | The proposed method achieves performance improvements on two popular datasets. |
Bilingual Tabular Inference: A Case Study on Indic Languages (2022.naacl-main)
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| Challenge: | Existing studies on Tabular Natural Language Inference (TNLI) focus on monolingual settings where tabular premise and hypothesis are in the same language. |
| Approach: | They propose a task where tabular premise and hypothesis are in two languages . they translate textual hypotheses from an English-indic TNLI dataset into eleven major languages - english and indic . |
| Outcome: | The proposed model performs well on a bilingual dataset in English and in 11 major Indian languages. |
Finding Needles in Images: Can Multi-modal LLMs Locate Fine Details? (2025.acl-long)
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| Challenge: | Recent advances in Multi-modal Large Language Models (MLLMs) have fundamentally transformed how machines understand and reason about visual information. |
| Approach: | They propose a benchmark to evaluate MLLMs' ability to locate and reason about fine-grained details within complex documents including newspapers, menus, and lecture images. |
| Outcome: | The proposed method improves on existing methods and shows that it can handle fine-grained document understanding tasks. |