Papers by Sneha Mondal

2 papers
CoCoa: An Encoder-Decoder Model for Controllable Code-switched Generation (2022.emnlp-main)

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Challenge: Generating code-switched text with fine-grained control on the degree of code-witching and the lexical choices used to convey formality has been well-explored.
Approach: They propose to generate code-switched text with fine-grained control on the degree of code-changing and lexical choices used to convey formality.
Outcome: The proposed model can be invoked at test-time to synthesize code-switched text faithful to syntactic and lexical attributes relevant to code-witching.
DIMSIM: Distilled Multilingual Critics for Indic Text Simplification (2024.findings-acl)

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Challenge: Existing approaches to improve the quality of responses generated by large language models (LLMs) however, these critique-refine steps require multiple expensive LLM calls.
Approach: They propose to use critique distillation to train critic models that are trained on input-critique pairs generated by an LLM.
Outcome: The proposed model trains two separate critics that focus on lexical and structure complexity, and is more effective than using an LLM directly as a critic in both 0-shot and few-shot settings.

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