TST$^\mathrm{R}$: Target Similarity Tuning Meets the Real World

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.

Code Generation Sentence +2

Augmented Embeddings for Custom Retrievals

no code implementations • 9 Oct 2023 • Anirudh Khatry, Yasharth Bajpai, Priyanshu Gupta, Sumit Gulwani, Ashish Tiwari

The flavor of retrieval typically used in classical applications can be termed as homogeneous and relaxed, where queries and corpus elements are both natural language (NL) utterances (homogeneous) and the goal is to pick most relevant elements from the corpus in the Top-K, where K is large, such as 10, 25, 50 or even 100 (relaxed).

Information Retrieval Retrieval

From Words to Code: Harnessing Data for Program Synthesis from Natural Language

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.

Program Synthesis

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