1 citations · 2 across the 11 of their papers we have counts for
11 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…
Contrast Matters: Understanding Robustness of In-Context Fine-Tuning to Target-Context Relatedness
Deeptanshu Malu, Deevyanshu Malu, Aditya Nemiwal +1
In-context fine-tuning (IC-Train), training an LLM with labeled examples in-context, is increasingly used in place of standard fine-tuning for domain adaptation and continual absor…
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.…
Robust Root Cause Diagnosis using In-Distribution Interventions
Lokesh Nagalapatti, Ashutosh Srivastava, Sunita Sarawagi +1
Diagnosing the root cause of an anomaly in a complex interconnected system is a pressing problem in today's cloud services and industrial operations. We propose In-Distribution Int…
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh +1
Treatment effect estimation involves assessing the impact of different treatments on individual outcomes. Current methods estimate Conditional Average Treatment Effect (CATE) using…
Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
Annie D'souza, Swetha M, Sunita Sarawagi
Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training…
Text-to-SQL Calibration: No Need to Ask -- Just Rescale Model Probabilities
Ashwin Ramachandran, Sunita Sarawagi
Calibration is crucial as large language models (LLMs) are increasingly deployed to convert natural language queries into SQL for commercial databases. In this work, we investigate…
SALSA: Speedy ASR-LLM Synchronous Aggregation
Ashish Mittal, Darshan Prabhu, Sunita Sarawagi +1
Harnessing pre-trained LLMs to improve ASR systems, particularly for low-resource languages, is now an emerging area of research. Existing methods range from using LLMs for ASR err…