16 citations · 30 across the 4 of their papers we have counts for
4 papers
Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning
Kavi Gupta, Kate Sanders, Armando Solar-Lezama
While LLMs have revolutionized the field of machine learning due to their high performance on a strikingly wide range of problems, they are also known to hallucinate false answers…
SPARLING: Learning Latent Representations with Extremely Sparse Activations
Kavi Gupta, Osbert Bastani, Armando Solar-Lezama
Real-world processes often contain intermediate state that can be modeled as an extremely sparse activation tensor. In this work, we analyze the identifiability of such sparse and…
Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis
Kavi Gupta, Peter Ebert Christensen, Xinyun Chen +1
The use of deep learning techniques has achieved significant progress for program synthesis from input-output examples. However, when the program semantics become more complex, it…
Synthetic Datasets for Neural Program Synthesis
Richard Shin, Neel Kant, Kavi Gupta +4
The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e.g. input-output behavior. Many current approaches…