1 citations · 1 across the 3 of their papers we have counts for
14 papers
Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decoding
Smit Jivani, Sarvam Maheshwari, Sunita Sarawagi
Large language models (LLMs) have revolutionized Text-to-SQL generation, allowing users to query structured data using natural language with growing ease. Yet, real-world deploymen…
Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling
Deeptanshu Malu, Deevyanshu Malu, Aditya Nemiwal +1
We investigate training strategies that co-develop in-context learning (ICL) and in-weights learning (IWL), and the ability to switch between them based on context relevance. Altho…
Masked Diffusion Models are Secretly Learned-Order Autoregressive Models
Prateek Garg, Bhavya Kohli, Sunita Sarawagi
Masked Diffusion Models (MDMs) have emerged as one of the most promising paradigms for generative modeling over discrete domains. It is known that MDMs effectively train to decode…
Retrieval and Augmentation of Domain Knowledge for Text-to-SQL Semantic Parsing
Manasi Patwardhan, Ayush Agarwal, Shabbirhussain Bhaisaheb +3
The performance of Large Language Models (LLMs) for translating Natural Language (NL) queries into SQL varies significantly across databases (DBs). NL queries are often expressed u…
TFMAdapter: Lightweight Instance-Level Adaptation of Foundation Models for Forecasting with Covariates
Afrin Dange, Sunita Sarawagi
Time Series Foundation Models (TSFMs) have recently achieved state-of-the-art performance in univariate forecasting on new time series simply by conditioned on a brief history of p…
From Search To Sampling: Generative Models For Robust Algorithmic Recourse
Prateek Garg, Lokesh Nagalapatti, Sunita Sarawagi
Algorithmic Recourse provides recommendations to individuals who are adversely impacted by automated model decisions, on how to alter their profiles to achieve a favorable outcome.…