8 papers · 1 filter
TACO: Task-Aware Column Description Generation Using LLMs
Ting Cai, Rakesh R. Menon, Yiru Chen +8
Generating accurate and informative column descriptions (e.g. "membership status of customers" for the column name "cust_mem") is essential for a wide range of downstream NLP tasks…
INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models
Aum Kendapadi, Kerem Zaman, Rakesh R. Menon +1
Large language models (LLMs) excel at answering questions but remain passive learners-absorbing static data without the ability to question and refine knowledge. This paper explore…
DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers
Rakesh R. Menon, Shashank Srivastava
Despite their high predictive accuracies, current machine learning systems often exhibit systematic biases stemming from annotation artifacts or insufficient support for certain cl…
SocialGaze: Improving the Integration of Human Social Norms in Large Language Models
Anvesh Rao Vijjini, Rakesh R. Menon, Jiayi Fu +2
While much research has explored enhancing the reasoning capabilities of large language models (LLMs) in the last few years, there is a gap in understanding the alignment of these…
Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers
Kangda Wei, Sayan Ghosh, Rakesh R. Menon +1
Recent approaches have explored language-guided classifiers capable of classifying examples from novel tasks when provided with task-specific natural language explanations, instruc…
MaNtLE: Model-agnostic Natural Language Explainer
Rakesh R. Menon, Kerem Zaman, Shashank Srivastava
Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance. While previous approaches…