Papers by Manasi Patwardhan
Program Synthesis for Complex QA on Charts via Probabilistic Grammar Based Filtered Iterative Back-Translation (2023.findings-eacl)
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Shabbirhussain Bhaisaheb, Shubham Paliwal, Rajaswa Patil, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff
| Challenge: | Current chart-based Question Answering approaches address structural, visual or simple data retrieval-type questions with fixed-vocabulary answers. |
| Approach: | They employ a neural semantic parser to transform NL questions into SQL programs . they use a probabilistic context-free grammar to generate NL queries from a schema . |
| Outcome: | The proposed approach achieves State-of-the-Art (SOTA) results on reasoning-based queries. |
Understanding Advertisements with BERT (2020.acl-main)
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| Challenge: | Recent results have shown that the embedded scene-text in the image holds a vital cue for this task. |
| Approach: | They propose to use the embedded scene-text as a cue for a sentence-pair classification task based on CVPR 2018 challenge dataset on advertisement understanding to rank valid and negatively sampled invalid interpretations of an image. |
| Outcome: | The proposed model achieves 89.69% accuracy, an improvement of 4.7% on the previous model. |
SciSketch: An Open-source Framework for Automated Schematic Diagram Generation in Scientific Papers (2025.emnlp-demos)
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| Challenge: | SCISKETCH is an open-source framework that supports two automated workflows for schematic diagram generation using foundation models. |
| Approach: | They propose an open-source framework that supports two automated workflows for schematic diagram generation using foundation models. |
| Outcome: | The open-source framework outperforms several state-of-the-art foundation models in generating schematic diagrams for scientific papers. |
MIR: Methodology Inspiration Retrieval for Scientific Research Problems (2025.acl-long)
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Aniketh Garikaparthi, Manasi Patwardhan, Aditya Sanjiv Kanade, Aman Hassan, Lovekesh Vig, Arman Cohan
| Challenge: | Existing methods for generating ideas rely on grounding the discovery process within the literature, but their effectiveness varies significantly with the quality and nature of the retrieved literature. |
| Approach: | They construct a methodological inspiration retrieval task using a citation-based methodology adjacency graph and embed an "intuitive prior'' into dense retrievers. |
| Outcome: | The proposed method achieves significant gains in Recall@3 and mAP over strong baselines. |
AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research (2025.acl-long)
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Yilun Zhao, Weiyuan Chen, Zhijian Xu, Manasi Patwardhan, Chengye Wang, Yixin Liu, Lovekesh Vig, Arman Cohan
| Challenge: | a benchmark designed to evaluate the capabilities of LLMs in designing ablation studies for scientific research is available online. |
| Approach: | They propose to use a benchmark to evaluate LLMs' ability to design ablation studies . they investigate whether current automated evaluation methods are not reliable . |
| Outcome: | The benchmark compared leading LLMs with human experts on generating detailed ablation study designs . the results show that current evaluation methods are not reliable for the task . |
From Monolingual to Multilingual FAQ Assistant using Multilingual Co-training (D19-61)
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Mayur Patidar, Surabhi Kumari, Manasi Patwardhan, Shirish Karande, Puneet Agarwal, Lovekesh Vig, Gautam Shroff
| Challenge: | Recent research on cross-lingual transfer shows state-of-the-art results on benchmark datasets using pre-trained language representation models like BERT. |
| Approach: | They propose a method to augment an annotated dataset with machine translations in target languages and fine-tune the PLRM jointly. |
| Outcome: | The proposed approach provides consistent gains on multiple benchmark datasets while requiring a single model for multiple languages. |
SciMDR: Advancing Scientific Multimodal Document Reasoning (2026.acl-long)
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| Challenge: | Current models struggle to provide reliable assistance in real-world scientific workflows because evidence is distributed across long, multimodal documents. |
| Approach: | They propose a framework for QA Synthesis and document-scale regrounding that generates faithful, isolated QA pairs and reasoning on focused segments. |
| Outcome: | The proposed framework achieves significant improvements across multiple QA benchmarks, particularly in tasks requiring complex document-level reasoning. |
SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literature (2026.eacl-long)
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| Challenge: | Existing retrieval-augmented generation methods overlook citation graph structure, adapt poorly to complex queries, and yield fragmented, hard-to-verify syntheses. |
| Approach: | They propose a retrieval-augmented generation framework that addresses these gaps by combining adaptive retrieval and symbolic reasoning. |
| Outcome: | Extensive experiments show that SciRAG outperforms prior systems in factual accuracy and synthesis quality. |
Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026.acl-long)
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Sihong Wu, Owen Jiang, Yilun Zhao, Tiansheng Hu, Yiling Ma, Kaiyan Zhang, Manasi Patwardhan, Arman Cohan
| Challenge: | Recent advances in large language models (LLMs) motivated methods that assist or automate different stages of peer review pipeline. |
| Approach: | They synthesize techniques to enhance peer review generation and after-review tasks aligned to reviews. |
| Outcome: | The proposed methods improve the peer review process by fine-tuning strategies, agent-based systems, and emerging paradigms. |
Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation (2026.findings-acl)
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| Challenge: | Language models are accelerating scientific research by automating hypothesis generation and implementation. |
| Approach: | They ask whether LMs can forecast the empirical success of research ideas before experiments . they frame evaluation as a reasoning task via Reinforcement Learning with Verifiable Rewards . |
| Outcome: | The proposed model outperforms off-the-shelf models in 77.1% of the evaluations . the model outpersforms GPT-5 in the evaluation of 11,488 idea pairs . |
Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable capabilities across a variety of scientific tasks, such as answering questions about scientific papers, writing scientific papers and retrieving related works. |
| Approach: | They propose a taxonomy of limitation types in scientific research with a focus on AI to evaluate their ability to support early-stage feedback and complement human peer review. |
| Outcome: | The proposed model enhances the ability of LLM systems to generate limitations in research papers, enabling them to provide more concrete and constructive feedback. |
IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery (2025.acl-demo)
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| Challenge: | Recent work on automated hypothesis generation focuses on multi-agent frameworks and extending test-time compute, but none incorporates human-in-the-loop (HITL) integration. |
| Approach: | They propose an open-source platform to enable researchers to leverage LLM-assisted scientific ideation. |
| Outcome: | The proposed system empowers researchers with greater control throughout ideation process. |
RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation (2026.findings-acl)
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| Challenge: | Prior studies show that large language models can draft fluent reviews but they miss specific issues, show shallow analysis, and produce generic phrasing. |
| Approach: | They propose a task that targets actionable review feedback generation and places existing peer review rebuttal at the center of learning. |
| Outcome: | The proposed model improves on a large dataset that maps review segments to rebuttal segments that address them, with perspective labels and impact categories that order author uptake. |