Frustrated with Code Quality Issues? LLMs can Help!

no code implementations • 22 Sep 2023 • Nalin Wadhwa, Jui Pradhan, Atharv Sonwane, Surya Prakash Sahu, Nagarajan Natarajan, Aditya Kanade, Suresh Parthasarathy, Sriram Rajamani

We present a tool, CORE (short for COde REvisions), architected using a pair of LLMs organized as a duo comprised of a proposer and a ranker.

Instruction Following Program Repair

Landmarks and Regions: A Robust Approach to Data Extraction

no code implementations • 11 Apr 2022 • Suresh Parthasarathy, Lincy Pattanaik, Anirudh Khatry, Arun Iyer, Arjun Radhakrishna, Sriram Rajamani, Mohammad Raza

We propose a new approach to extracting data items or field values from semi-structured documents.

Program Synthesis

Programming by Rewards

no code implementations • 14 Jul 2020 • Nagarajan Natarajan, Ajaykrishna Karthikeyan, Prateek Jain, Ivan Radicek, Sriram Rajamani, Sumit Gulwani, Johannes Gehrke

The goal of the synthesizer is to synthesize a "decision function" $f$ which transforms the features to a decision value for the black-box component so as to maximize the expected reward $E[r \circ f (x)]$ for executing decisions $f(x)$ for various values of $x$.

Program Synthesis

Debugging Machine Learning Tasks

no code implementations • 23 Mar 2016 • Aleksandar Chakarov, Aditya Nori, Sriram Rajamani, Shayak Sen, Deepak Vijaykeerthy

Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data.

BIG-bench Machine Learning