Papers by Sourav Medya
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation (2025.emnlp-industry)
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Weizhi Zhang, Liangwei Yang, Wooseong Yang, Henry Peng Zou, Yuqing Liu, Ke Xu, Sourav Medya, Philip S. Yu
| Challenge: | Existing algorithms for collaborative filtering are limited by their computational demands and latency. |
| Approach: | They propose a framework to integrate pre-trained LLM embeddings into CF models through selective initialization strategies. |
| Outcome: | The proposed framework improves recommendation performance while maintaining low computational costs. |
An Experimental Analysis on Evaluating Patent Citations (2024.emnlp-main)
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| Challenge: | Graph Neural Networks (GNNs)-based methods can predict patent citations using only patent text. |
| Approach: | They propose to use Graph Neural Networks to predict citations for patents based on their semantic similarities to generate a semantic graph of patents. |
| Outcome: | The proposed methods produce 94% recall for patents with high citations and outperform baselines. |
A Survey on Patent Analysis: From NLP to Multimodal AI (2025.acl-long)
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| Challenge: | Recent advances in pretrained language models and large language models have demonstrated transformative capabilities across diverse domains. |
| Approach: | They propose a taxonomy for categorization based on tasks in the patent life cycle . they introduce a novel taxonomies for categorizing based upon tasks in patent life cycles . |
| Outcome: | The proposed method is based on tasks in the patent life cycle and provides a taxonomy for categorization based upon tasks in patent life cycles. |
Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach (2025.acl-long)
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| Challenge: | Temporal information extraction from unstructured text is challenging due to complex clinical language, long documents, and sparse annotations. |
| Approach: | They propose a method for extracting clinical events and their temporal relations from unstructured text using the I2B2 2012 Temporal Relations Challenge corpus. |
| Outcome: | The proposed method improves state-of-the-art temporal information extraction with 5.5% improvement in tempeval F1 score over previous best and 8.9% improvement on long-range relations. |
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context (2026.acl-long)
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| Challenge: | Existing explanation methods for graph neural networks struggle to generate interpretable, fine-grained rationales. |
| Approach: | They propose a lightweight framework that uses large language models to generate interpretable explanations for GNNs. |
| Outcome: | The proposed framework generates interpretable explanations for GNN predictions using large language models. |
DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding (2025.findings-emnlp)
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| Challenge: | patent images often lack comprehensive visual context and semantic information, authors say . recent advances in vision-language models offer promising opportunities for patent analysis . |
| Approach: | They develop a framework for design patent analysis using large-scale patent dataset . they validate the effectiveness of DesignCLIP across various downstream tasks . |
| Outcome: | The proposed framework outperforms baseline and SOTA models on all tasks. |
Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models (2026.findings-acl)
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| Challenge: | Graph problems require reasoning over explicit structure, permutation invariance, and computationally complex relationships, creating a mismatch with the representations of text-based models. |
| Approach: | They propose a human-interpretable structural encoding strategy that injects graph structure directly into natural language prompts. |
| Outcome: | The proposed method improves performance on synthetic and real-world datasets. |