4 citations · 5 across the 10 of their papers we have counts for
10 papers
Distributions In, Distributions Out: The Case for Soft-Label Training
Agamdeep Singh, Ashish Tiwari, Hosein Hasanbeig +1
Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotators into a majority vote. On tasks…
ConDABench: Interactive Evaluation of Language Models for Data Analysis
Avik Dutta, Priyanshu Gupta, Hosein Hasanbeig +6
Real-world data analysis tasks often come with under-specified goals and unclean data. User interaction is necessary to understand and disambiguate a user's intent, and hence, esse…
STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBack
Shashank Kirtania, Naman Gupta, Priyanshu Gupta +7
Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Aug…
MetaReflection: Learning Instructions for Language Agents using Past Reflections
Priyanshu Gupta, Shashank Kirtania, Ananya Singha +4
The popularity of Large Language Models (LLMs) have unleashed a new age ofLanguage Agents for solving a diverse range of tasks. While contemporary frontier LLMs are capable enough…
TST: Target Similarity Tuning Meets the Real World
Anirudh Khatry, Sumit Gulwani, Priyanshu Gupta +4
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. I…
Augmented Embeddings for Custom Retrievals
Anirudh Khatry, Yasharth Bajpai, Priyanshu Gupta +2
Information retrieval involves selecting artifacts from a corpus that are most relevant to a given search query. The flavor of retrieval typically used in classical applications ca…
GrACE: Generation using Associated Code Edits
Priyanshu Gupta, Avishree Khare, Yasharth Bajpai +6
Developers expend a significant amount of time in editing code for a variety of reasons such as bug fixing or adding new features. Designing effective methods to predict code edits…
Overwatch: Learning Patterns in Code Edit Sequences
Yuhao Zhang, Yasharth Bajpai, Priyanshu Gupta +8
Integrated Development Environments (IDEs) provide tool support to automate many source code editing tasks. Traditionally, IDEs use only the spatial context, i.e., the location whe…
A Probabilistic Framework for Knowledge Graph Data Augmentation
Jatin Chauhan, Priyanshu Gupta, Pasquale Minervini
We present NNMFAug, a probabilistic framework to perform data augmentation for the task of knowledge graph completion to counter the problem of data scarcity, which can enhance the…
Unbiased Loss Functions for Extreme Classification With Missing Labels
Erik Schultheis, Mohammadreza Qaraei, Priyanshu Gupta +1
The goal in extreme multi-label classification (XMC) is to tag an instance with a small subset of relevant labels from an extremely large set of possible labels. In addition to the…