no code implementations • 18 Feb 2024 • Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu, Benjamin Zorn, José Cambronero, Andrew D. Gordon, Vu Le, Elnaz Nouri, Nadia Polikarpova, Advait Sarkar, Brian Slininger, Neil Toronto, Jack Williams
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users.
no code implementations • 13 Dec 2023 • Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Gust Verbruggen
Multi-modality promises to unlock further uses for large language models.
1 code implementation • 6 Nov 2023 • Hai Phan, Cindy Le, Vu Le, Yihui He, Anh Totti Nguyen
DeepFace-EMD (Phan et al. 2022) reaches state-of-the-art accuracy on out-of-distribution data by first comparing two images at the image level, and then at the patch level.
no code implementations • 26 Oct 2023 • Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, Elnaz Nouri, Mohammad Raza, Gust Verbruggen
Writing such rules is often challenging for users as it requires them to understand and implement the underlying logic.
no code implementations • 26 Oct 2023 • Anirudh Khatry, Sumit Gulwani, Priyanshu Gupta, Vu Le, Ananya Singha, Mukul Singh, Gust Verbruggen
Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs) to improve performance.
no code implementations • 26 Oct 2023 • Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, Gust Verbruggen
Imagine a developer who can only change their last line of code, how often would they have to start writing a function from scratch before it is correct?
no code implementations • 16 Oct 2023 • Ananya Singha, José Cambronero, Sumit Gulwani, Vu Le, Chris Parnin
Inspired by prior work, we generate a collection of self-supervised structural tasks (e. g. navigate to a cell and row; transpose the table) and evaluate the performance differences when using 8 formats.
no code implementations • 21 Aug 2023 • Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, Gust Verbruggen
DataVinci learns regular-expression-based patterns that cover a majority of values in a column and reports values that do not satisfy such patterns as data errors.
no code implementations • 14 Aug 2023 • Mukul Singh, Jose Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, Gust Verbruggen
After the user provides one or two formatted cells as examples, CORNET generates formatting rule suggestions for the user to apply to the spreadsheet.
1 code implementation • 12 Jul 2023 • Matthew Anderson, Vu Le
We advance the Cohn-Umans framework for developing fast matrix multiplication algorithms.
no code implementations • 2 May 2023 • Anirudh Khatry, Joyce Cahoon, Jordan Henkel, Shaleen Deep, Venkatesh Emani, Avrilia Floratou, Sumit Gulwani, Vu Le, Mohammad Raza, Sherry Shi, Mukul Singh, Ashish Tiwari
Existing approaches have utilized data context in a limited way by simply adding relevant information from the input data into the prompts sent to the LLM.
no code implementations • 31 Jan 2023 • Harshit Joshi, Abishai Ebenezer, José Cambronero, Sumit Gulwani, Aditya Kanade, Vu Le, Ivan Radiček, Gust Verbruggen
We evaluate FLAME on formula repair, formula completion, and similarity-based formula retrieval.
no code implementations • 29 Sep 2022 • Jialu Zhang, José Cambronero, Sumit Gulwani, Vu Le, Ruzica Piskac, Gustavo Soares, Gust Verbruggen
We propose to use a large language model trained on code, such as Codex, to build an APR system -- MMAPR -- for introductory Python programming assignments.
no code implementations • 24 Aug 2022 • Harshit Joshi, José Cambronero, Sumit Gulwani, Vu Le, Ivan Radicek, Gust Verbruggen
We show that RING can outperform language-specific repair engines for three of these languages.
no code implementations • 11 Aug 2022 • Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, Mohammad Raza, Gust Verbruggen
Since we are the first to introduce conditional formatting, we compare CORNET to a wide range of symbolic and neural baselines adapted from related domains.
no code implementations • 24 Jul 2022 • Rohan Bavishi, Harshit Joshi, José Pablo Cambronero Sánchez, Anna Fariha, Sumit Gulwani, Vu Le, Ivan Radicek, Ashish Tiwari
To address this problem, we developed LaMirage, a LAst-MIle RepAir-engine GEnerator that combines symbolic and neural techniques to perform last-mile repair in low-code formula languages.
2 code implementations • ICLR 2022 • Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, Sumit Gulwani
Then, Synchromesh feeds the examples to a pre-trained language model and samples programs using Constrained Semantic Decoding (CSD): a general framework for constraining the output to a set of valid programs in the target language.
no code implementations • 3 Sep 2021 • Kia Rahmani, Mohammad Raza, Sumit Gulwani, Vu Le, Daniel Morris, Arjun Radhakrishna, Gustavo Soares, Ashish Tiwari
Examples provide a precise but incomplete specification, and natural language provides an ambiguous but more "complete" task description.