Papers by Ankush 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. |
BI-Bench : A Comprehensive Benchmark Dataset and Unsupervised Evaluation for BI Systems (2025.acl-industry)
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| Challenge: | Existing benchmarks focus on isolated components rather than addressing the broader needs of BI users. |
| Approach: | They propose a holistic, end-to-end benchmarking framework that categorizes queries into descriptive, diagnostic, predictive, and prescriptive types, aligning with practical BI needs. |
| Outcome: | The proposed framework assesses BI systems on quality, relevance, depth of insights based on queries categorized into descriptive, diagnostic, predictive, and prescriptive types . |
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. |
Development of an Enterprise-Grade Contract Understanding System (2021.naacl-industry)
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Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
| Challenge: | Currently, legal contract review remains an expensive and arduous process. |
| Approach: | They describe a commercial system designed and deployed for contract understanding that enables legal professionals to review contracts. |
| Outcome: | The proposed system is used by a wide range of enterprise users and solves three major challenges. |
Goal-Driven Data Story, Narrations and Explanations (2025.naacl-industry)
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| Challenge: | Unlike existing tools, our system addresses the ambiguity of vague, multi-line queries, setting a new benchmark in data storytelling by tackling complexities no existing system comprehensively handles. |
| Approach: | They propose a system that processes and interprets vague, open-ended, and multi-line complex queries, transforming them into coherent, actionable data stories. |
| Outcome: | The proposed system processes and interprets vague, open-ended, and multi-line complex queries, transforming them into coherent, actionable data stories. |
Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain (2022.lrec-1)
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Ankush Agarwal, Raj Gite, Shreya Laddha, Pushpak Bhattacharyya, Satyanarayan Kar, Asif Ekbal, Prabhjit Thind, Rajesh Zele, Ravi Shankar
| Challenge: | Existing Question Answering systems for commercial aviation use a large number of documents . a Knowledge Graph (KG) guided Deep Learning (DL) based system can be used to query the documents based on accident reports . |
| Approach: | They propose a Knowledge Graph (KG) guided Deep Learning (DL) based Question Answering system to cater to these requirements. |
| Outcome: | The proposed system achieves 7% and 40% increase in accuracy over existing systems. |