Papers by Anant Gupta
Distillation of encoder-decoder transformers for sequence labelling (2023.findings-eacl)
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| Challenge: | despite the strong trend in NLP to explore the use of large language models, there is still limited work evaluating prompting and decoding mechanisms for SL tasks. |
| Approach: | They propose a hallucination-free framework for sequence tagging that is especially suited for distillation. |
| Outcome: | The proposed framework performs well across multiple sequence labelling datasets and in a few-shot learning scenario. |
Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributes (2023.acl-industry)
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| Challenge: | E-commerce websites often don’t label or mislabel attributes of products . |
| Approach: | They propose a multi-modal product attribute generation system that extracts product attributes from the product pages of eCommerce stores by using both text and images. |
| Outcome: | The proposed model improves the recall@90P accuracy by 10.16% and 6.9 from the state-of-the-art models. |
CoCoA: Confidence- and Context-Aware Adaptive Decoding for Resolving Knowledge Conflicts in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing contrastive decoding methods that handle conflict lack adaptability and can degrade performance in low conflict settings. |
| Approach: | They propose a token-level algorithm for principled conflict resolution and enhanced faithfulness that resolves conflict by utilizing confidence-aware measures and the generalized divergence between parametric and contextual distributions. |
| Outcome: | The proposed algorithm achieves 9.2 points on average in QA, summarization, and long-form question answering (LFQA) benchmarks and improves factuality by 2.5 points on the key benchmarks. |
CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling (2026.findings-acl)
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| Challenge: | Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. |
| Approach: | They propose a lifelong hierarchical topic model based on incremental probabilistic concept formation that constructs semantic hierarchies online without predefining the number of topics. |
| Outcome: | The proposed model achieves strong topic coherence, stable topics over time, and high-quality hierarchies without predefining the number of topics. |
Leveraging Contextual Information for Effective Entity Salience Detection (2024.findings-naacl)
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Rajarshi Bhowmik, Marco Ponza, Atharva Tendle, Anant Gupta, Rebecca Jiang, Xingyu Lu, Qian Zhao, Daniel Preotiuc-Pietro
| Challenge: | Prior work on salient entity detection focused on machine learning models that require heavy feature engineering. |
| Approach: | They propose to fine-tune medium-sized language models with a cross-encoder style architecture to achieve significant performance gains over feature engineering approaches. |
| Outcome: | The proposed model fine-tunes medium-sized pre-trained language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. |