Papers by Pritom Saha Akash
Coordinated Topic Modeling (2022.emnlp-main)
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| Challenge: | Rather than finding arbitrary topics, people often want to explore the text based on some welldefined topics. |
| Approach: | They propose a problem called coordinated topic modeling that imitates human behavior while describing a text corpus. |
| Outcome: | The proposed model is superior to baseline models on multiple domains. |
MiniELM: A Lightweight and Adaptive Query Rewriting Framework for E-Commerce Search Optimization (2025.findings-acl)
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| Challenge: | Existing methods for rewriting query terms struggle with natural language understanding . generative methods face high inference latency and cost in offline settings . |
| Approach: | They propose a hybrid pipeline for rewriting query queries using offline knowledge distillation and online reinforcement learning. |
| Outcome: | The proposed pipeline improves query relevance, diversity, adaptability and cost-effective evaluation without manual annotations on Amazon ESCI dataset. |
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models (2025.findings-emnlp)
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| Challenge: | Extract-Refine-Retrieve-Read is a query optimization framework for large language models . it is designed to bridge the pre-retrieval information gap in Retriev-Augmented Generation systems . |
| Approach: | They propose a framework to extract parametric knowledge from Large Language Models and refine them using a specialized query optimizer. |
| Outcome: | The extract-refine-retrieve-read framework outperforms baselines on QA datasets . it is designed to meet the knowledge requirements of large language models (LLMs) |
ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation (2024.findings-emnlp)
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| Challenge: | Existing iterative retrieval-augmented generation approaches struggle to delve deeply into each facet of complex queries. |
| Approach: | They propose a framework that employs a tree-structured retrieval approach to enhance the depth and relevance of retrieved content. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multiple datasets and a newly introduced dataset. |
Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling (2025.acl-long)
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| Challenge: | Existing topic modeling models struggle in low-resource settings where data is limited . et al., 2003: domain adaptation for low-source topic modeling is challenging in low resources . |
| Approach: | They propose a domain adaptation framework that disentangles domaininvariant and domain-specific components to improve topic adaptation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on low-resource datasets on diverse datasets. |
Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs (2024.findings-emnlp)
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| Challenge: | Existing topic models often lack sufficient word co-occurrence in short texts, resulting in incoherent topics. |
| Approach: | They propose to use large language models to extend short texts into more detailed sequences before applying topic modeling to solve semantic inconsistency problem. |
| Outcome: | The proposed approach significantly outperforms current state-of-the-art topic models on real-world datasets with extreme data sparsity. |
Unsupervised Open-domain Keyphrase Generation (2023.acl-long)
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| Challenge: | Existing models that generate keyphrases without human-labeled data are lacking in this area. |
| Approach: | They propose a model that consists of two modules that can be built in an unsupervised fashion and can perform consistently across domains. |
| Outcome: | The proposed model performs consistently across domains and narrows the gap between supervised and unsupervised models down to about 16%. |
ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation (2025.acl-long)
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| Challenge: | Existing methods for keyphrase prediction rely on heuristicically defined importance scores . existing methods lack consideration for time efficiency . |
| Approach: | They propose an unsupervised keyphrase generation model that combines informativeness and phraseness modules. |
| Outcome: | The proposed model outperforms baseline models and achieves 89% of the performance of a supervised model for top 10 predictions. |
Domain Representative Keywords Selection: A Probabilistic Approach (2022.findings-acl)
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| Challenge: | a probabilistic approach to select a subset of a target domain representative keywords is crucial for many downstream tasks in natural language processing. |
| Approach: | They propose a probabilistic approach to select a subset of a target domain representative keywords from a candidate set, contrasting with a context domain. |
| Outcome: | The proposed approach provides more importance to distinctive keywords than common keywords contrasting with the context domain. |