Prottay Kumar Adhikary - Doctoral Researcher at IIT Delhi

@inproceedings{10.1145/3774904.3792988, author = {Adhikary, Prottay Kumar and Rawat, Reena and Chakraborty, Tanmoy}, title = {coTherapist: A Behavior-Aligned Small Language Model Framework to Support Mental Healthcare Experts}, year = {2026}, isbn = {9798400723070}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3774904.3792988}, doi = {10.1145/3774904.3792988}, abstract = {Access to mental healthcare is increasingly strained by workforce shortages and rising demand, motivating the development of intelligent systems that can support mental healthcare experts. We introduce coTherapist, a unified framework utilizing a small language model to emulate core therapeutic competencies through domain-specific fine-tuning, retrieval augmentation, and agentic reasoning. Evaluation on clinical queries demonstrates that coTherapist generates more relevant and clinically grounded responses than contemporary baselines. Using our novel T-BARS rubric and psychometric profiling, we confirm coTherapist exhibits high empathy and therapist-consistent personality traits. Furthermore, human evaluation by domain experts validates that coTherapist delivers accurate, trustworthy, and safe responses. coTherapist was deployed and tested by clinical experts. Collectively, these findings demonstrate that small models can be engineered to exhibit expert-like behavior, offering a scalable pathway for digital mental health tools.}, booktitle = {Proceedings of the ACM Web Conference 2026}, pages = {9529–9540}, numpages = {12}, keywords = {mental health care, small language models, retrieval augmented generation, agentic framework, web-based interaction}, location = {United Arab Emirates}, series = {WWW '26} }

@article{adhikary2026brokeneyes, title={Through BrokenEyes: How Eye Disorders Impact Face Detection?}, author={Adhikary, Prottay Kumar}, journal={arXiv preprint arXiv:2602.23212}, year={2026} }

@article{mandal2025mentalhealthdatasets, title={A Comprehensive Review of Datasets for Clinical Mental Health AI Systems}, author={Mandal, Aishik and Adhikary, Prottay Kumar and Arnaout, Hiba and Gurevych, Iryna and Chakraborty, Tanmoy}, journal={arXiv preprint arXiv:2508.09809}, year={2025} }

@article {Adhikary2025.06.25.25330252, author = {Adhikary, Prottay Kumar and Singh, Sahajpreet and Singh, Suruchi and Sharma, Panna and Soni, Pankhuri and Choudhary, Rashmi and Saxena, Charu and Chauhan, Prachi and Gupta, Swati Kedia and Deb, Koushik Sinha and Singh, Salam Michael and Chakraborty, Tanmoy}, title = {Towards Richer AI-Assisted Psychotherapy Note-Making and Performance Benchmarking}, elocation-id = {2025.06.25.25330252}, year = {2025}, doi = {10.1101/2025.06.25.25330252}, publisher = {Cold Spring Harbor Laboratory Press}, abstract = {Psychotherapy note-making is crucial for effective patient care. However, traditional formats such as SOAP (Subjective, Objective, Assessment, and Plan) and BIRP (Behavior, Intervention, Response, and Plan) often fail to capture the nuanced complexities of therapeutic sessions, as they primarily focus on surface-level details and lack a comprehensive understanding of the patient{\textquoteright}s history, mental status, and therapeutic process. While recent advances in Artificial Intelligence (AI) and Large Language Models (LLMs) show promise in clinical documentation, their application in psychotherapy note summarisation remains unexplored. We present iCARE (identifiers, Chief Concerns and Clinical History, Assessment and Analysis, Risk and Crisis, Engagement and Next Steps), a comprehensive framework for AI-assisted psychotherapy documentation that addresses these limitations. iCARE comprises of 17 clinically relevant aspects, developed collaboratively with mental health professionals, and aligned with established guidelines. We further introduce PATH (Psychotherapy Aspects and Treatment History summary), a novel dataset of annotated therapy sessions. Through extensive benchmarking with 11 LLMs, including both open and closed-source models, we evaluate their performance across different note-taking aspects using automatic and human evaluation metrics. Our results show that closed-source models like Gemini Pro and GPT4o-mini excel in various aspects, with Gemini Pro achieving superior human evaluation scores. Notably, all models struggle with temporal reasoning and complex therapeutic interpretations. The findings suggest that current LLMs can assist in basic documentation but require improvements in handling longitudinal therapeutic relationships and aspects that require deeper clinical understanding and interpretative reasoning. This work advances mental health care documentation while emphasising the need for continued clinical expertise in psychotherapy note summarisation.Competing Interest StatementThe authors have declared no competing interest.Funding StatementTanmoy Chakraborty acknowledges the support of Tower Research Capital Markets toward using machine learning for social good and Rajiv Khemani Young Faculty Chair Professorship in Artificial Intelligence.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll conversation transcripts used in this study and all source code for benchmarking experiments are publicly available in https://github.com/proadhikary/iCARE/. https://github.com/proadhikary/iCARE}, URL = {https://www.medrxiv.org/content/early/2025/06/25/2025.06.25.25330252}, eprint = {https://www.medrxiv.org/content/early/2025/06/25/2025.06.25.25330252.full.pdf}, journal = {medRxiv} }

@Article{info:doi/10.2196/71977, author="Adhikary, Prottay Kumar and Motiyani, Isha and Oke, Gayatri and Joshi, Maithili and Pathak, Kanupriya and Singh, Salam Michael and Chakraborty, Tanmoy", title="Menstrual Health Education Using a Specialized Large Language Model in India: Development and Evaluation Study of MenstLLaMA", journal="J Med Internet Res", year="2025", month="Jul", day="16", volume="27", pages="e71977", keywords="menstrual health education; artificial intelligence; large language model; cultural sensitivity; health equity; digital health", abstract="Background: The quality and accessibility of menstrual health education (MHE) in low- and middle-income countries, including India, remain inadequate due to persistent challenges (eg, poverty, social stigma, and gender inequality). While community-driven initiatives have sought to raise awareness, artificial intelligence offers a scalable and efficient solution for disseminating accurate information. However, existing general-purpose large language models (LLMs) are often ill-suited for this task, tending to exhibit low accuracy, cultural insensitivity, and overly complex responses. To address these limitations, we developed MenstLLaMA---a specialized LLM tailored to the Indian context and designed to deliver MHE empathetically, supportively, and accessibly. Objective: We aimed to develop and evaluate MenstLLaMA---a specialized LLM tailored to deliver accurate, culturally sensitive MHE---and assess its effectiveness in comparison to existing general-purpose models. Methods: We curated MENST---a novel, domain-specific dataset comprising 23,820 question-answer pairs aggregated from medical websites, government portals, and health education resources. This dataset was systematically annotated with metadata capturing age groups, regions, topics, and sociocultural contexts. MenstLLaMA was developed by fine-tuning Meta-LLaMA-3-8B-Instruct, using parameter-efficient fine-tuning with low-rank adaptation to achieve domain alignment while minimizing computational overhead. We benchmarked MenstLLaMA against 9 state-of-the-art general-purpose LLMs, including GPT-4o, Claude-3, Gemini 1.5 Pro, and Mistral. The evaluation followed a multilayered framework: (1) automatic evaluation using standard natural language processing metrics (BLEU [Bilingual Evaluation Understudy], METEOR [Metric for Evaluation of Translation with Explicit Ordering], ROUGE-L [Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence], and BERTScore [Bidirectional Encoder Representations from Transformers Score]); (2) evaluation by clinical experts (N=18), who rated 200 expert-curated queries for accuracy and appropriateness; (3) medical practitioner interaction through the ISHA (Intelligent System for Menstrual Health Assistance) interactive chatbot, assessing qualitative dimensions (eg, relevance, understandability, preciseness, correctness, and context sensitivity); and (4) a user study with volunteer participants (N=200), who evaluated MenstLLaMA in 15- to 20-minute randomized sessions, rating the system across 7 qualitative user satisfaction metrics. Results: MenstLLaMA achieved the highest scores in BLEU (0.059) and BERTScore (0.911), outperforming GPT-4o (BLEU: 0.052, BERTScore: 0.896) and Claude-3 (BERTScore: 0.888). Clinical experts preferred MenstLLaMA's responses over gold-standard answers in several culturally sensitive cases. In medical practitioners' evaluations using the ISHA---the chat interface powered by MenstLLaMA---the model scored 3.5 in relevance, 3.6 in understandability, 3.1/5 in preciseness, 3.5/5 in correctness, and 4.0/5 in context sensitivity. User evaluations indicated even stronger results, with ratings of 4.7/5 for understandability, 4.3/5 for relevance, 4.28/5 for preciseness, 4.1/5 for correctness, 4.6/5 for tone, 4.2/5 for flow, and 3.9/5 for context sensitivity. Conclusions: MenstLLaMA demonstrates exceptional accuracy, empathy, and user satisfaction within the domain of MHE, bridging critical gaps left by general-purpose LLMs. Its potential for integration into broader health education platforms positions it as a transformative tool for menstrual well-being. Future research could explore its long-term impact on public perception and menstrual hygiene practices, while expanding demographic representation, enhancing context sensitivity, and integrating multimodal and voice-based interactions to improve accessibility across diverse user groups. ", issn="1438-8871", doi="10.2196/71977", url="https://www.jmir.org/2025/1/e71977", url="https://doi.org/10.2196/71977" }

@article{adhikary2024exploring, title={Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: Benchmark Study}, author={Adhikary, Prottay Kumar and Srivastava, Aseem and Kumar, Shivani and Singh, Salam Michael and Manuja, Puneet and Gopinath, Jini K and Krishnan, Vijay and Gupta, Swati Kedia and Deb, Koushik Sinha and Chakraborty, Tanmoy}, journal={JMIR Mental Health}, volume={11}, pages={e57306}, year={2024}, publisher={JMIR Publications Toronto, Canada} }

@InProceedings{adhikary-EtAl:2023:ijcnlp, author = {Adhikary, Prottay Kumar and Sugandhi, Bandaru and Ghimire, Subhojit and Pal, Santanu and Pakray, Partha}, title = {TRAVID: An End-to-End Video Translation Framework}, booktitle = {System Demonstrations}, month = {November}, year = {2023}, address = {Bali, Indonesia}, publisher = {Asian Federation of Natural Language Processing}, pages = {1--9} }

@inproceedings{vetagiri-etal-2023-cnlp, title = "{CNLP}-{NITS} at {S}em{E}val-2023 Task 10: Online sexism prediction, {PREDHATE}!", author = "Vetagiri, Advaitha and Adhikary, Prottay and Pakray, Partha and Das, Amitava", booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.113", doi = "10.18653/v1/2023.semeval-1.113", pages = "815--822", abstract = "Online sexism is a rising issue that threatens women{'}s safety, fosters hostile situations, and upholds social inequities. We describe a task SemEval-2023 Task 10 for creating English-language models that can precisely identify and categorize sexist content on internet forums and social platforms like Gab and Reddit as well to provide an explainability in order to address this problem. The problem is divided into three hierarchically organized subtasks: binary sexism detection, sexism by category, and sexism by fine-grained vector. The dataset consists of 20,000 labelled entries. For Task A, pertained models like Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), which is called CNN-BiLSTM and Generative Pretrained Transformer 2 (GPT-2) models were used, as well as the GPT-2 model for Task B and C, and have provided experimental configurations. According to our findings, the GPT-2 model performs better than the CNN-BiLSTM model for Task A, while GPT-2 is highly accurate for Tasks B and C on the training, validation and testing splits of the training data provided in the task. Our proposed models allow researchers to create more precise and understandable models for identifying and categorizing sexist content in online forums, thereby empowering users and moderators.", }

@article{vetagiri2023leveraging, title={Leveraging GPT-2 for automated classification of online sexist content}, author={Vetagiri, Advaitha and Adhikary, Prottay Kumar and Pakray, Partha and Das, Amitava}, year={2023} }

@article{ADHIKARY20232350, title = {Dzongkha Handwritten Digit Recognition using Machine Learning Techniques}, journal = {Procedia Computer Science}, volume = {218}, pages = {2350-2358}, year = {2023}, note = {International Conference on Machine Learning and Data Engineering}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2023.01.210}, url = {https://www.sciencedirect.com/science/article/pii/S1877050923002107}, author = {Prottay Kumar Adhikary and Pankaj Dadure and Pradipta Saha and Tawmo and Partha Pakray}, keywords = {Dzongkha Language, Character Recognition, Digit Recognition, Handwritten Characters, Machine Learning}, abstract = {Handwritten digit recognition has recently gained importance, attracting many researchers due to its use in various machine learning and computer vision applications. As technology and science progressing, there is a need for a system to recognize the handwritten script in several real-time applications to reduce human effort. There a lot of work has been done on the recognition and generation of handwritten digits of high-resource languages such as English. However, insufficient work has been done on Dzongkha digits recognition, as Dzongkha digits are low-resource and more complex than English patterns. This paper aims to perform handwritten character recognition of Dzongkha digit using several machine learning techniques. The unavailability of the Dzongkha handwritten digit dataset is the prime motivation behind this work. To facilitates the recognition of Dzongkha handwritten digit, we have collected the data of Dzongkha handwritten digit from indigenous and non-indigenous people of Bhutan and provided the dataset for further research. Moreover, we have used several machine algorithms, including a support vector machine, K-nearest neighbor, and decision tree. Among these algorithms, the support vector machine classification algorithm has achieved a remarkable result with an accuracy of 98.29%.} }

@Article{Laskar2022, author={Laskar, Sahinur Rahman and Gogoi, Abinash and Dutta, Samudranil and Adhikary, Prottay Kumar and Nath, Prachurya and Pakray, Partha and Bandyopadhyay, Sivaji}, title={Investigation of negation effect for English--Assamese machine translation}, journal={S{\={a}}dhan{\={a}}}, year={2022}, month={Nov}, day={14}, volume={47}, number={4}, pages={238}, abstract={Computational linguistics deals with the computational modelling of natural languages, in which machine translation is a popular task. The aim of machine translation is to automatically translate one natural language into another, which minimizes the linguistic barrier of different linguistic backgrounds. The data-driven approach of machine translation, namely, neural machine translation achieves state-of-the-art results on different language pairs, however it needs a sufficient amount of parallel training data to attain reasonable translation performance. In this work, we have explored different machine translation models on a low-resource English--Assamese language pair and investigated different sources of errors, particularly due to negation in English-to-Assamese and Assamese-to-English translation. Negation is a universal, essential feature of human language that has a substantial impact on the semantics of a statement. Moreover, a rule-based approach is proposed in the data preprocessing step which handles modal-verb negation problem that shows significant improvement in translation performance in terms of automatic and manual evaluation scores.}, issn={0973-7677}, doi={10.1007/s12046-022-01965-5}, url={https://doi.org/10.1007/s12046-022-01965-5} }

@inproceedings{nath-etal-2022-image, title = "Image Caption Generation for Low-Resource {A}ssamese Language", author = "Nath, Prachurya and Adhikary, Prottay Kumar and Dadure, Pankaj and Pakray, Partha and Manna, Riyanka and Bandyopadhyay, Sivaji", booktitle = "Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)", month = nov, year = "2022", address = "Taipei, Taiwan", publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)", url = "https://aclanthology.org/2022.rocling-1.33", pages = "263--272", abstract = "Image captioning is a prominent Artificial Intelligence (AI) research area that deals with visual recognition and a linguistic description of the image. It is an interdisciplinary field concerning how computers can see and understand digital images{\&} videos, and describe them in a language known to humans. Constructing a meaningful sentence needs both structural and semantic information of the language. This paper highlights the contribution of image caption generation for the Assamese language. The unavailability of an image caption generation system for the Assamese language is an open problem for AI-NLP researchers, and it{'}s just an early stage of the research. To achieve our defined objective, we have used the encoder-decoder framework, which combines the Convolutional Neural Networks and the Recurrent Neural Networks. The experiment has been tested on Flickr30k and Coco Captions dataset, which have been originally present in the English language. We have translated these datasets into Assamese language using the state-of-the-art Machine Translation (MT) system for our designed work.", }

@InProceedings{10.1007/978-3-031-10766-5_26, author="Adhikary, Prottay Kumar and Manna, Riyanka and Laskar, Sahinur Rahman and Pakray, Partha", editor="Mukhopadhyay, Somnath and Sarkar, Sunita and Dutta, Paramartha and Mandal, Jyotsna Kumar and Roy, Sudipta", title="Ontology-Based Healthcare Hierarchy Towards Chatbot", booktitle="Computational Intelligence in Communications and Business Analytics", year="2022", publisher="Springer International Publishing", address="Cham", pages="326--335", abstract="Ontology refers to relationship-based hierarchical descriptions of concepts within a particular domain. Ontology, in the field of medicine, describes the concepts of medical terminologies and the relation between them, thus, enabling the sharing of medical knowledge. This paper aims to develop an ontology-based healthcare hierarchy and point out the research scope towards the chatbot application. The research scope includes the integration of the ontology-based healthcare hierarchy in the chatbot application by the establishment of relationships among individuals and real-world entities.", isbn="978-3-031-10766-5" }

@InProceedings{10.1007/978-3-031-10766-5_22, author="Tawmo and Adhikary, Prottay Kumar and Dadure, Pankaj and Pakray, Partha", editor="Mukhopadhyay, Somnath and Sarkar, Sunita and Dutta, Paramartha and Mandal, Jyotsna Kumar and Roy, Sudipta", title="An Empirical Analysis on Abstractive Text Summarization", booktitle="Computational Intelligence in Communications and Business Analytics", year="2022", publisher="Springer International Publishing", address="Cham", pages="280--287", abstract="With the massive growth of blogs, news stories, and reports, extracting useful information from such a large quantity of textual documents has become a difficult task. Automatic text summarization is an excellent approach for summarising these documents. Text summarization aims to condense large documents into concise summaries while preserving essential information and meaning. A variety of fascinating summarising models have been developed to achieve state-of-the-art performance in terms of fluency, human readability, and semantically meaningful summaries. In this paper, we have investigated the OpenNMT tool for task text summarization. The OpenNMT is the encoder-decoder-based neural machine translation model which has been fine-tuned for the task of abstractive text summarization. The proposed OpenNMT based text summarization approach has been tested on freely available dataset such as CNNDM {\&} MSMO dataset and depicts their proficiency in terms of ROUGE and BLEU score.", isbn="978-3-031-10766-5" }

@dataset{tawmo_2022_6271560, author = {Tawmo and Prottay Kumar Adhikary and Pankaj Dadure and Partha Pakray}, title = {Dzongkha Handwritten Digit Dataset}, month = feb, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.6271560}, url = {https://doi.org/10.5281/zenodo.6271560} }

@inproceedings{laskar-etal-2021-neural, title = "Neural Machine Translation for {T}amil{--}{T}elugu Pair", author = "Laskar, Sahinur Rahman and Paul, Bishwaraj and Adhikary, Prottay Kumar and Pakray, Partha and Bandyopadhyay, Sivaji", booktitle = "Proceedings of the Sixth Conference on Machine Translation", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.wmt-1.29", pages = "284--287", abstract = "The neural machine translation approach has gained popularity in machine translation because of its context analysing ability and its handling of long-term dependency issues. We have participated in the WMT21 shared task of similar language translation on a Tamil-Telugu pair with the team name: CNLP-NITS. In this task, we utilized monolingual data via pre-train word embeddings in transformer model based neural machine translation to tackle the limitation of parallel corpus. Our model has achieved a bilingual evaluation understudy (BLEU) score of 4.05, rank-based intuitive bilingual evaluation score (RIBES) score of 24.80 and translation edit rate (TER) score of 97.24 for both Tamil-to-Telugu and Telugu-to-Tamil translations respectively.", }