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Sandipan Dandapat

8 accepted papers

2025

Enhancing Zero-shot Chain of Thought Prompting via Uncertainty-Guided Strategy Selection

Shanu Kumar, Saish Mendke, Karody Lubna Abdul Rahman, Santosh Kurasa, Parag Agrawal, Sandipan Dandapat
COLING 2025main

Chain-of-thought (CoT) prompting has significantly enhanced the the capability of large language models (LLMs) by structuring their reasoning processes. However, existing methods face critical limitations: handcrafted demonstrations require extensive human expertise, while trigger phrases are prone…

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2024

INMT-Lite: Accelerating Low-Resource Language Data Collection via Offline Interactive Neural Machine Translation

Harshita Diddee, Anurag Shukla, Tanuja Ganu, Vivek Seshadri, Sandipan Dandapat, Monojit Choudhury +1
COLING 2024main

A steady increase in the performance of Massively Multilingual Models (MMLMs) has contributed to their rapidly increasing use in data collection pipelines. Interactive Neural Machine Translation (INMT) systems are one class of tools that can utilize MMLMs to promote such data collection in several u…

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2023

Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models

Raghav Jain, Daivik Sojitra, Arkadeep Acharya, Sriparna Saha, Adam Jatowt, Sandipan Dandapat
EMNLP 2023long main

Temporal reasoning represents a vital component of human communication and understanding, yet remains an underexplored area within the context of Large Language Models (LLMs). Despite LLMs demonstrating significant proficiency in a range of tasks, a comprehensive, large-scale analysis of their tempo…

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2022

Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models

Kabir Ahuja, Shanu Kumar, Sandipan Dandapat, Monojit Choudhury
ACL 2022long

Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages, though the performance varies from language to language depending on the pivot language(s) used for fine-tuning. In this work, we build upon some of the ex…

Cited by 22Source ↗PDF ↗code ↗Scholar ↗
2022

On the Calibration of Massively Multilingual Language Models

Kabir Ahuja, Sunayana Sitaram, Sandipan Dandapat, Monojit Choudhury
EMNLP 2022main

Massively Multilingual Language Models (MMLMs) have recently gained popularity due to their surprising effectiveness in cross-lingual transfer. While there has been much work in evaluating these models for their performance on a variety of tasks and languages, little attention has been paid on how w…

Cited by 19Source ↗PDF ↗code ↗Scholar ↗
2022

On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data

Kabir Ahuja, Monojit Choudhury, Sandipan Dandapat
NAACL 2022long

Borrowing ideas from Production functions in micro-economics, in this paper we introduce a framework to systematically evaluate the performance and cost trade-offs between machine-translated and manually-created labelled data for task-specific fine-tuning of massively multilingual language models. W…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2022

”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer

Shanu Kumar, Sandipan Dandapat, Monojit Choudhury
NAACL 2022findings

Few-shot transfer often shows substantial gain over zero-shot transfer (CITATION), which is a practically useful trade-off between fully supervised and unsupervised learning approaches for multilingual pretained model-based systems. This paper explores various strategies for selecting data for annot…

Cited by 6Source ↗PDF ↗Scholar ↗