Papers by Niloy Ganguly
Learning Faster with Better Tokens: Parameter-Efficient Vocabulary Adaptation for Specialized Text Summarization (2026.acl-long)
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| Challenge: | a new approach to adapt generalist models to expert domains is needed to overcome this problem. |
| Approach: | They propose a parameter-efficient domain adaptation approach that combines vocabulary adaptation with pretraining for LLM-based text summarization. |
| Outcome: | The proposed approach reduces training time by 35-55% over continual pretraining and reduces parameter counts up to 37% w.r.t expansion-only methods. |
CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text (2023.findings-emnlp)
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| Challenge: | Existing methods to solve procedural reasoning tasks are limited by the prior art. |
| Approach: | They propose a domain-specific, continual pre-training framework that learns from a large set of procedural recipes. |
| Outcome: | The proposed framework outperforms baselines on recipes (in-domain) but is able to generalize to open-domain procedural NLP tasks. |
Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling (2024.naacl-long)
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Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh, Manjunath Hegde, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
| Challenge: | Existing methods to automatically annotate relevant numerals (GAAP metrics) occurring in financial documents are not cost-effective nor scalable. |
| Approach: | They propose a generative paradigm for annotating GAAP metrics with XBRL tags using metric metadata and a parameter efficient model using LoRA. |
| Outcome: | The proposed model outperforms baseline models on two financial numeric labeling datasets and outperformed several strong baseline models. |
TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya (2024.lrec-main)
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| Challenge: | Existing annotated datasets for NLP tasks in languages with limited resources are limited. |
| Approach: | They propose to use machine translation to convert existing Tigrinya dataset into a Tigrina dataset in SQuAD format. |
| Outcome: | The proposed dataset is an expert-annotated Tigrinya dataset with 2,685 question-answer pairs covering 122 diverse topics. |
Financial Numeric Extreme Labelling: A dataset and benchmarking (2023.findings-acl)
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Soumya Sharma, Subhendu Khatuya, Manjunath Hegde, Afreen Shaikh, Koustuv Dasgupta, Pawan Goyal, Niloy Ganguly
| Challenge: | In 2019, the SEC mandates that all public companies file periodic financial statements that should contain numerals annotated with a particular label. |
| Approach: | They propose to use a dataset to automate the assignment of a label to a particular numeral span in a sentence from an extremely large label set. |
| Outcome: | The proposed solution outperforms the previous approaches but is less frequent than the pipeline solution. |
Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMs (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for high-resource languages (HRLs). |
| Approach: | They propose to reduce the cost of processing LRLs by code-mixing, translation, and transliteration of LRL to HRLs to ensure that predictive and generative qualities are not compromised. |
| Outcome: | The proposed model reduces the cost of processing LRLs while ensuring that predictive and generative qualities are not compromised. |
How Robust Are the QA Models for Hybrid Scientific Tabular Data? A Study Using Customized Dataset (2024.lrec-main)
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| Challenge: | Existing tabular QA models are lacking in understanding their robustness on scientific information. |
| Approach: | They propose a dataset to assess the robustness of tabular QA models on scientific hybrid tabular data. |
| Outcome: | The proposed model performs well on scientific tables and text, while the best score is 0.462. |
A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection (2022.findings-naacl)
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| Challenge: | Existing approaches to detect novel intents have been tested in the last decade. |
| Approach: | They propose a framework to detect multiple novel intents with budgeted human annotation cost. |
| Outcome: | The proposed framework outperforms baseline methods in terms of accuracy and F1-score on a set of benchmark datasets. |
Adaptive BPE Tokenization for Enhanced Vocabulary Adaptation in Finetuning Pretrained Language Models (2024.findings-emnlp)
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| Challenge: | Current vocabulary adaptation approaches append the target domainspecific vocabulary (V DOMAIN) at the end of the PLM vocabulary. |
| Approach: | They propose a vocabulary adaptation scheme that appends a target domain-specific vocabulary (V DOMAIN) at the end of the PLM vocabulary. |
| Outcome: | The proposed approach improves by 3.57% (in terms of accuracy) and 1.87% (royal-L) over various classification and summarization tasks. |
Order-Based Pre-training Strategies for Procedural Text Understanding (2024.naacl-short)
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| Challenge: | Procedural text is difficult to understand due to the changing attributes of entities in the context. |
| Approach: | They propose sequence-based pre-training methods to enhance procedural understanding in natural language processing by using ordered instructions to guide individuals through a task. |
| Outcome: | The proposed methods improve on two datasets in the datasets NPN-Cooking and ProPara domains respectively. |
Entropy-guided Vocabulary Augmentation of Multilingual Language Models for Low-resource Tasks (2023.findings-acl)
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| Challenge: | Multilingual language models (MLLMs) support low-resource languages (LRLs) but LRL words are under-represented in wordpiece/subword vocabularies, leading to low task accuracy . |
| Approach: | They propose an entropy-based vocabulary augmented language model to detect LRL words with undesirable wordpiece segmentations. |
| Outcome: | The proposed model improves performance and limits on wordpiece augmentation strategies for multiple diverse LRLs. |
Evaluation of LLMs in Medical Text Summarization: The Role of Vocabulary Adaptation in High OOV Settings (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have been successful in medical text summarization . however, they do not perform fine-grained evaluations under difficult settings . |
| Approach: | They show that large language models show a significant performance drop for data points with high concentration of out-of-vocabulary words or with high novelty. |
| Outcome: | The proposed model shows a significant performance drop for data points with high concentration of out-of-vocabulary words or with high novelty. |
Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification (2025.findings-acl)
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| Challenge: | Existing approaches to multi-label text classification are limited by textual data. |
| Approach: | They propose a domain-agnostic generative model framework for multi-label text classification that generates predefined label descriptions and matches them to predefined labels. |
| Outcome: | The proposed model achieves 13.94% and 24.85% performance over all datasets. |
Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) are recognized for their exceptional generative capabilities and versatility across various tasks. |
| Approach: | They conduct a comprehensive benchmarking of LLM inference energy across a wide range of NLP tasks to determine the impact of different models, tasks, prompts, and system-related factors on inference. |
| Outcome: | The proposed model energy benchmarks show that quantization and optimal batch sizes can significantly reduce energy usage. |
Brevity is the soul of sustainability: Characterizing LLM response lengths (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) consume significant energy and carbon emissions due to their inference processes. |
| Approach: | They first benchmark 12 decoder-only LLMs across 5 datasets and then analyze LLM responses to determine their quality. |
| Outcome: | The proposed methods can reduce the length of responses while preserving the quality of the LLMs. |
Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents (2025.emnlp-main)
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Ankan Mullick, Sombit Bose, Rounak Saha, Ayan Kumar Bhowmick, Aditya Vempaty, Prasenjit Dey, Ravi Kokku, Pawan Goyal, Niloy Ganguly
| Challenge: | Unlike highlights (fragmented key points) and traditional summaries, spotlights selectively emphasize intriguing content to foster deeper reader engagement with the source material. |
| Approach: | They propose a novel paradigm for information extraction that selectively emphasizes intriguing content to foster deeper reader engagement with the source material. |
| Outcome: | The proposed model improves readability and boosts engagement value of the original document. |
***YesBut***: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models (2024.emnlp-main)
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Abhilash Nandy, Yash Agarwal, Ashish Patwa, Millon Das, Aman Bansal, Ankit Raj, Pawan Goyal, Niloy Ganguly
| Challenge: | Existing Vision-Language models perform poorly on satirical image detecting tasks . satire and humor are powerful tools to highlight issues, provoke thought, and encourage critical perspective . |
| Approach: | They propose to use a dataset to evaluate satirical images and satire images to detect satiric images . they also propose to generate the reason behind the image being satiral by generating one half of the image to be satisfying . |
| Outcome: | The proposed dataset contains 2547 images, 1084 satirical and 1463 non-satirically, with different artistic styles. |
ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts (2022.emnlp-main)
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Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma, Manjunath Hegde, Afreen Shaikh, Shivani Shrivastava, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
| Challenge: | ECTSum is a dataset for bullet-point summarization of earnings calls hosted by publicly traded companies. |
| Approach: | They propose a dataset with transcripts of earnings calls and bullet point summaries derived from Reuters articles. |
| Outcome: | The proposed dataset compares transcripts of earnings calls hosted by publicly traded companies with experts-written bullet point summaries derived from Reuters articles . |
tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets (2021.naacl-main)
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| Challenge: | Existing stance detection datasets are complex deep neural networks, making them difficult to interpret. |
| Approach: | They propose a new large dataset free of such biases and demonstrate its aptness on existing stance detection systems. |
| Outcome: | The proposed model achieves human-level performance on the WT–WT dataset and more than two-third accuracy on other datasets. |
On The Persona-based Summarization of Domain-Specific Documents (2024.findings-acl)
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Ankan Mullick, Sombit Bose, Rounak Saha, Ayan Bhowmick, Pawan Goyal, Niloy Ganguly, Prasenjit Dey, Ravi Kokku
| Challenge: | In an ever-expanding world of domain-specific knowledge, summarization of information is a complex task . persona-based summarizing of domain specific information by humans is deemed not preferred . |
| Approach: | They propose a framework for efficient training of a small foundation LLM on a healthcare corpus. |
| Outcome: | The proposed framework fine-tunes a domain-specific small foundation LLM using a healthcare corpus and evaluates its quality using AI-based critiquing. |
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification (N19-1)
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| Challenge: | Existing fact-checking datasets do not provide manual annotations for sentence-level evidence. |
| Approach: | They propose a task-agnostic pipelined system that extracts textual evidence that supports or refutes a factual claim from Wikipedia pages. |
| Outcome: | The proposed system achieves state-of-the-art results on the FEVER dataset. |
Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text (P19-1)
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| Challenge: | Existing sentiment detection methods are trained on sentiment-labeled monolingual text. |
| Approach: | They propose a method for synthesizing labeled code-switched text from monolingual text. |
| Outcome: | The proposed method improves sentiment labeling accuracy for three languages. |
Benchmarking the Energy Savings with Speculative Decoding Strategies (2026.findings-eacl)
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Rohit Dutta, Paramita Koley, Soham Poddar, Janardan Misra, Sanjay Podder, Naveen Balani, Saptarshi Ghosh, Niloy Ganguly
| Challenge: | Existing studies on speculative decoding have focused on the energy requirements of these models, despite their utility and utility. |
| Approach: | They propose to analyze the energy requirements of speculative decoding strategies and analyze how various factors influence the energy optimizations. |
| Outcome: | The proposed approach reduces decoding time while offloading a substantial portion of the sequential generation to a smaller, more efficient model. |
Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs (D19-1)
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| Challenge: | Recent studies have shown that structured domain knowledge can be used for textual inference tasks in the medical domain. |
| Approach: | They propose to integrate structured domain knowledge into a knowledge graph for the Medical NLI task. |
| Outcome: | The proposed approach improves the baseline BioELMo architecture for the Medical NLI task. |
MutantPrompt: Prompt Optimization via Mutation Under a Budget on Modest-sized LMs (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have revolutionized the way we learn and process information, but identifying optimal prompts remains a challenge for low-resource languages. |
| Approach: | They propose a framework that leverages multi-armed bandit algorithms to efficiently identify optimal prompts tailored to low-resource languages. |
| Outcome: | The proposed framework is able to find optimal prompts for low-resource languages and significantly improves performance across multiple low-level tasks. |
Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval (2025.emnlp-main)
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| Challenge: | Numerical reasoning remains a challenging area for large language models (LLMs). |
| Approach: | They propose a two-step framework to enhance LLM's capabilities in financial numerical reasoning by using a generative retriever and context-aware program of thought prompting. |
| Outcome: | The proposed model surpasses previous benchmarks with execution accuracy improvements of 5.98% and 4.05%, respectively. |
Efficient Continual Pre-training of LLMs for Low-resource Languages (2025.naacl-industry)
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| Challenge: | Open-source large language models (LLMs) are a promising tool for low-resource languages . however, there is still a substantial performance gap between high-resourced languages and LRLs . |
| Approach: | They develop an algorithm to select a subset of texts from a larger corpus and use it to select tokens for LLMs. |
| Outcome: | The proposed algorithm reduces the cost of continual pre-training (CPT) with large amounts of language-specific data. |