Papers by Soumya Suvra Ghosal
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from related Example Banks (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive few-shot learning capabilities through in-context learning. |
| Approach: | They propose a novel Alternating Minimization approach for example selection that improves ICL performance on low-resource Indic languages. |
| Outcome: | The proposed approach outperforms existing frameworks for retrieving examples on low-resource Indic languages. |
IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning (2024.emnlp-main)
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| Challenge: | Existing prompt-tuning frameworks lack interpretability, limiting their ability to understand compositional nature of images. |
| Approach: | They propose a prompt-tuning method that integrates compositional attributes into manual prompts to enhance image-text alignment scores. |
| Outcome: | The proposed method improves CoOp performance by 7.35% across 10 diverse datasets. |
Engagement Undermines Safety: How Stereotypes and Toxicity Shape Humor in Language Models (2026.eacl-long)
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| Challenge: | Large language models are increasingly used for creative writing and engagement content, raising safety concerns about their outputs. |
| Approach: | They evaluate how funniness optimization in large language models couples with harmful content by jointly measuring humor, stereotypicality, and toxicity. |
| Outcome: | The proposed model couples humor, stereotypicality, and toxicity with harmful outputs . the results suggest a bias amplification loop between generators and evaluators . |
RELIC: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples (2025.findings-emnlp)
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Soumya Suvra Ghosal, Vaibhav Singh, Akash Ghosh, Soumyabrata Pal, Subhadip Baidya, Sriparna Saha, Dinesh Manocha
| Challenge: | a new reward model for low-resource Indic languages is proposed . a preference-based training approach is prohibitively expensive, authors say . |
| Approach: | a new in-context learning framework is proposed to train a retriever to select in-constext examples from low-resource Indic languages. |
| Outcome: | a new in-context learning framework for reward modeling in low-resource Indic languages is developed . the proposed framework outperforms existing examples on three preference datasets . |