Papers by Vaibhav Kumar
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering (2022.naacl-main)
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| Challenge: | Existing approaches to intent detection rely on epoch wise clustering and classification based on labeled and unlabeled data. |
| Approach: | They propose an end-to-end deep contrastive clustering algorithm that jointly updates model parameters and cluster centers via supervised and self-supervised learning. |
| Outcome: | The proposed approach outperforms baselines on five public datasets and human-in-the-loop variant for practical deployment. |
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection (2022.findings-naacl)
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| Challenge: | Existing methods to identify all possible user intents at design time are expensive and require storage of past data. |
| Approach: | They propose to continually train an intent detector on new intents while maintaining performance on prior intents. |
| Outcome: | The proposed method outperforms exemplar replay-based approaches on lifelong intent detection tasks and achieves state-of-the-art on four public datasets. |
De-Mixing Sentiment from Code-Mixed Text (P19-2)
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| Challenge: | Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. |
| Approach: | They propose a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data using CNNs to generate subword representations for the sentences. |
| Outcome: | The proposed architecture achieves 83.54% accuracy and 0.827 F1 score on a benchmark dataset. |
Improving Tool Retrieval by Leveraging Large Language Models for Query Generation (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) have shown great promise in common sense language understanding, conversational fluency, and reasoning. |
| Approach: | They propose to use Large Language Models to generate a retrieval query and embed it into the prompt to find relevant tools via a nearest-neighbor search. |
| Outcome: | The proposed method improves retrieval for in-domain (seen tools) and out-of-domain settings. |
Making Information Seeking Easier: An Improved Pipeline for Conversational Search (2020.findings-emnlp)
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| Challenge: | Existing tools for conversational information seeking (CIS) do not support conversational contexts. |
| Approach: | They propose a highly effective pipeline for passage retrieval in a conversational search setting using a BERT-based classifier and a multi-view reranking component. |
| Outcome: | The proposed pipeline achieves 14.8% performance improvement over the current state-of-the-art pipeline and surpasses the Oracle. |
ClarQ: A large-scale and diverse dataset for Clarification Question Generation (2020.acl-main)
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| Challenge: | Existing datasets hinder development of large-scale models capable of generating and utilising clarification questions. |
| Approach: | They propose a bootstrapping framework that utilises a neural network architecture to classify clarification questions based on post-comment tuples extracted from stackexchange. |
| Outcome: | The proposed framework aims to increase the accuracy of the classifier and increase recall of clarification questions by applying it to question-answering tasks. |
Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT: A Case Study On Bhili-Hindi-English Parallel Corpus (2025.findings-emnlp)
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| Challenge: | linguistic diversity of India poses significant machine translation challenges, authors say . underrepresented tribal languages like Bhili lack high-quality linguistic resources . |
| Approach: | They introduce a Bhili-Hindi-English Parallel Corpus, the first and largest parallel corpus worldwide . they evaluated a wide range of proprietary and open-source MLLMs on bidirectional translation tasks . |
| Outcome: | The proposed corpus spans critical domains such as education, administration, and news. |
Controlled Text Generation with Hidden Representation Transformations (2023.findings-acl)
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| Challenge: | Using a con-trolled language model, we gain attribute control by modifying the hidden representation of thebase model through learning transformations. |
| Approach: | They propose a con-trolled language generation framework that gains attribute control bymodifying the hidden representation of thebase model through learned transformations. |
| Outcome: | The proposed framework outperforms all thebaselines in detoxification, positivesentiment steering, and text simplification while minimizing the loss in linguistic qualities. |
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects (2024.naacl-long)
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| Challenge: | X-Eval is a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Approach: | They introduce a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Outcome: | The proposed framework improves the model’s ability to follow evaluation instructions and enhances the learning stage to better assess text quality. |
Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs (2025.coling-main)
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Somnath Kumar, Vaibhav Balloli, Mercy Ranjit, Kabir Ahuja, Sunayana Sitaram, Kalika Bali, Tanuja Ganu, Akshay Nambi
| Challenge: | Large language models (LLMs) excel in diverse applications but still struggle with non-Latin scripts and low-resource languages. |
| Approach: | They propose a dynamic learning approach that optimizes prompt strategy, embedding model, and LLM per query at runtime. |
| Outcome: | The proposed approach achieves 10-15% improvements in multilingual performance over pre-trained models and 4x gains compared to fine-tuned, language-specific models. |
Planning and Editing What You Retrieve for Enhanced Tool Learning (2024.findings-naacl)
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| Challenge: | Existing methods for integrating external tools with Large Language Models fall short on effectively shortlisting relevant tools. |
| Approach: | They propose a plan-and-retrieve and edit-and ground paradigms for LLMs that decompose complex queries into actionable tasks. |
| Outcome: | The proposed paradigms significantly improve recall and NDCG in tool retrieval tasks, surpassing current state-of-the-art models. |
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings (2020.tacl-1)
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| Challenge: | Existing methods for debiasing word embeddings lack gender-based debiases . Existing approaches only reduce gender-related proximity biases by at least 42.02% . |
| Approach: | They propose a gender debiasing methodology that eliminates bias in word vectors and alters spatial distribution of neighboring vectors, achieving a bias-free setting while maintaining minimal semantic offset. |
| Outcome: | The proposed method outperforms the state-of-the-art in reducing proximity bias by at least 42.02% and reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task. |