22 citations · 34 across the 9 of their papers we have counts for
13 papers
Projected Distillation: On Learning to Solve Hard Problems from Close, Correct Targets
Shripad V. Deshmukh, Christian Bock, Kangrui Ruan +4
On-policy learning is central to post-training: training on a model's own generations limits distribution shift and helps preserve existing capabilities. On hard problems, however,…
Robust Learning of Diverse Code Edits
Tushar Aggarwal, Swayam Singh, Abhijeet Awasthi +2
Software engineering activities frequently involve edits to existing code. However, contemporary code language models (LMs) lack the ability to handle diverse types of code-edit re…
NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness
Manav Singhal, Tushar Aggarwal, Abhijeet Awasthi +2
Existing evaluation benchmarks of language models of code (code LMs) focus almost exclusively on whether the LMs can generate functionally-correct code. In real-world software engi…
Structured Case-based Reasoning for Inference-time Adaptation of Text-to-SQL parsers
Abhijeet Awasthi, Soumen Chakrabarti, Sunita Sarawagi
Inference-time adaptation methods for semantic parsing are useful for leveraging examples from newly-observed domains without repeated fine-tuning. Existing approaches typically bi…
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers
Abhijeet Awasthi, Ashutosh Sathe, Sunita Sarawagi
Text-to-SQL parsers typically struggle with databases unseen during the train time. Adapting parsers to new databases is a challenging problem due to the lack of natural language q…
Bootstrapping Multilingual Semantic Parsers using Large Language Models
Abhijeet Awasthi, Nitish Gupta, Bidisha Samanta +3
Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remain…
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study
Yash Khemchandani, Sarvesh Mehtani, Vaidehi Patil +3
Recent research in multilingual language models (LM) has demonstrated their ability to effectively handle multiple languages in a single model. This holds promise for low web-resou…
Teaching keyword spotters to spot new keywords with limited examples
Abhijeet Awasthi, Kevin Kilgour, Hassan Rom
Learning to recognize new keywords with just a few examples is essential for personalizing keyword spotting (KWS) models to a user's choice of keywords. However, modern KWS models…
Error-driven Fixed-Budget ASR Personalization for Accented Speakers
Abhijeet Awasthi, Aman Kansal, Sunita Sarawagi +1
We consider the task of personalizing ASR models while being constrained by a fixed budget on recording speaker-specific utterances. Given a speaker and an ASR model, we propose a…
What's in a Name? Are BERT Named Entity Representations just as Good for any other Name?
Sriram Balasubramanian, Naman Jain, Gaurav Jindal +2
We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input. We highlight that on sev…
Black-box Adaptation of ASR for Accented Speech
Kartik Khandelwal, Preethi Jyothi, Abhijeet Awasthi +1
We introduce the problem of adapting a black-box, cloud-based ASR system to speech from a target accent. While leading online ASR services obtain impressive performance on main-str…
Learning from Rules Generalizing Labeled Exemplars
Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal +1
In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision. We propose a rule-exemplar method for collecting human sup…
Parallel Iterative Edit Models for Local Sequence Transduction
Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal +2
We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). Recent approaches are base…