Papers by Piyush Mishra
When Big Models Train Small Ones: Label-Free Model Parity Alignment for Efficient Visual Question Answering using Small VLMs (2025.emnlp-main)
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| Challenge: | Large vision and language models have demonstrated remarkable performance in visual question answering tasks. |
| Approach: | They introduce a framework to optimize L-VLMs by leveraging unlabeled images . they conduct extensive experiments on four diverse VQA benchmarks . |
| Outcome: | The proposed framework improves L-VLMs on four visual question answering benchmarks. |
A Graphical Interface for Curating Schemas (2021.acl-demo)
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| Challenge: | Existing work on analyzing information extracted from documents has focused on examining the model understanding of complex schemas. |
| Approach: | They propose a curation interface that takes an IE system’s output in a pre-defined format and generates a graphical representation of its elements. |
| Outcome: | The proposed interface can be used to edit and prune schemas for complex events like Improvised Explosive Device (IED) based scenarios. |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
Beyond IVR: Benchmarking Customer Support LLM Agents for Business-Adherence (2026.eacl-industry)
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| Challenge: | Existing benchmarks focus on tool usage or task completion, overlooking an agent’s capacity to adhere to multi-step policies, navigate task dependencies, and remain robust to unpredictable user or environment behavior. |
| Approach: | They propose a benchmark to assess policy-aware agents in customer support using a dynamic-prompt agent and a static-promped agent that explicitly models policy control. |
| Outcome: | The proposed benchmark assesses agent's ability to adhere to multi-step policies, navigate task dependencies, and remain robust to unpredictable user or environment behavior. |