Papers by Sailik Sengupta
Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining (2023.eacl-main)
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| Challenge: | Existing studies on robustness of pretrained multilingual models are limited to the English language. |
| Approach: | They propose to use data augmentation and contrastive loss term to boost robustness of multilingual models in cross-lingual settings. |
| Outcome: | The proposed model outperforms existing models on clean and noisy data in the cross-lingual setting. |
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs (2024.naacl-long)
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| Challenge: | Flow-adhering planning algorithm for task oriented dialogs (TODs) is a task-oriented dialog (TO) that can be used for task planning and API usage. |
| Approach: | They propose a Flow-Adhering Planning algorithm that follows predefined flows and preserves API dependencies in task oriented dialogs. |
| Outcome: | The proposed algorithm outperforms other decoding and prompting-based baselines in task oriented dialogs. |
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders (2024.acl-long)
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| Challenge: | Conversational systems often rely on embedding models for intent classification and intent clustering tasks. |
| Approach: | They propose a toolkit that gives a more holistic view of intent embedding models by considering three tasks– (1) intent classification, (2) intent clustering, and (3) a novel triplet task. |
| Outcome: | The proposed model improves on the linguistic dimensions while affecting performance on downstream task metrics. |
DeAL: Decoding-time Alignment for Large Language Models (2025.acl-long)
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James Y. Huang, Sailik Sengupta, Daniele Bonadiman, Yi-An Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, Dan Roth
| Challenge: | Large Language Models (LLMs) are expected to generate content aligned with human preferences. |
| Approach: | They propose a framework that allows the user to customize reward functions and enables Decoding-time Alignment of LLMs (DeAL). |
| Outcome: | The proposed framework allows the user to customize reward functions and enables Decoding-time Alignment of LLMs. |
Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing (2023.findings-emnlp)
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| Challenge: | In task-oriented semantic parsing, the system aims to translate users’ utterances in natural language to machine-interpretable programs (API calls) However, Large Language Models (LLMs) are known to hallucinate and therefore pose a formidable challenge in constraining generated content. |
| Approach: | They propose to use large language models to translate user's utterances to machine-interpretable programs (API calls) they identify constraints violations in task-oriented utterrances and define fine-grained metrics that complement traditional ones. |
| Outcome: | The proposed methods reduce constraints violations and improve quality of the generated API calls, but require careful consideration given their implementation complexity and latency. |