Rahul Sharma


2024

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Towards Multi-Modal Co-Reference Resolution in Conversational Shopping Agents
Samuel Osebe | Prashan Wanigasekara | Thomas Gueudre | Thanh Tran | Rahul Sharma | Fan Yang | Qian Hu | Weitong Ruan | Emre Barut | Chengwei Su
Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024

The context of modern smart voice assistants is often multi-modal, where images, audio and video content are consumed by users simultaneously. In such a setup, co-reference resolution is especially challenging, and runs across modalities and dialogue turns. We explore the problem of multi-modal co-reference resolution in multi-turn dialogues and quantify the performance of multi-modal LLMs on a specially curated dataset of long, image-interleaved conversations between a voice assistant and human in a shopping use case. We propose a custom architecture for multi-modal embedding alignment using a novel parameter augmentation technique. Our proposed Parameter Augmented LLM approach shows a 4.9% absolute F1 improvement above a cross-attention baseline while reducing the number of parameters being trained by 4x.

2023

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Ranking LLM-Generated Loop Invariants for Program Verification
Saikat Chakraborty | Shuvendu Lahiri | Sarah Fakhoury | Akash Lal | Madanlal Musuvathi | Aseem Rastogi | Aditya Senthilnathan | Rahul Sharma | Nikhil Swamy
Findings of the Association for Computational Linguistics: EMNLP 2023

Synthesizing inductive loop invariants is fundamental to automating program verification. In this work we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariants. This can lead to a large number a calls to a program verifier to establish an invariant. To address this issue, we propose a re-ranking approach for the generated results of LLMs. We have designed a ranker that can distinguish between correct inductive invariants and incorrect attempts based on the problem definition. The ranker is optimized as a contrastive ranker. Experimental results demonstrate that this re-ranking mechanism significantly improves the ranking of correct invariants among the generated candidates, leading to a notable reduction in the number of calls to a verifier.

2022

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Federated Learning with Noisy User Feedback
Rahul Sharma | Anil Ramakrishna | Ansel MacLaughlin | Anna Rumshisky | Jimit Majmudar | Clement Chung | Salman Avestimehr | Rahul Gupta
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Machine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. Thishas led to growing concerns over user privacy, since human interaction data typically needs to be transmitted to the cloud in order to trainand improve such systems. Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy. However, since ML models are most commonly trained with label supervision, we need a way to extract labels on edge to make FL viable. In this work, we propose a strategy for training FL models using positive and negative user feedback. We also design a novel framework to study different noise patterns in user feedback, and explore how well standard noise-robust objectives can help mitigate this noise when training models in a federated setting. We evaluate our proposed training setup through detailed experiments on two text classification datasets and analyze the effects of varying levels of user reliability and feedback noise on model performance. We show that our method improves substantially over a self-training baseline, achieving performance closer to models trained with full supervision.

2014

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A hybrid approach for automatic clause boundary identification in Hindi
Rahul Sharma | Soma Paul
Proceedings of the Fifth Workshop on South and Southeast Asian Natural Language Processing

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A rule based approach for automatic clause boundary detection and classification in Hindi
Rahul Sharma
Proceedings of the Fifth Workshop on South and Southeast Asian Natural Language Processing

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Exploring the effects of Sentence Simplification on Hindi to English Machine Translation System
Kshitij Mishra | Ankush Soni | Rahul Sharma | Dipti Sharma
Proceedings of the Workshop on Automatic Text Simplification - Methods and Applications in the Multilingual Society (ATS-MA 2014)

2013

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Automatic Clause Boundary Annotation in the Hindi Treebank
Rahul Sharma | Soma Paul | Riyaz Ahmad Bhat | Sambhav Jain
Proceedings of the 27th Pacific Asia Conference on Language, Information, and Computation (PACLIC 27)