Prakhar Gupta

Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation

2022 · Findings of the Association for Computational Linguistics: NAACL 2022

Target-guided response generation enables dialogue systems to smoothly transition a conversation from a dialogue context toward a target sentence. Such control is useful for designing dialogue systems that direct a conversation toward specific goals, such …

Using In-Context Learning to Improve Dialogue Safety

2023 · arXiv (Cornell University)

While large neural-based conversational models have become increasingly proficient dialogue agents, recent work has highlighted safety issues with these systems. For example, these systems can be goaded into generating toxic content, which often perpetuates social …

Revisiting In-Context Learning with Long Context Language Models

2025

In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context.Previously, their context window size imposed a limit on the number of examples that can be …

Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features

2018

Matteo Pagliardini, Prakhar Gupta, Martin Jaggi. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Self-Refine: Iterative Refinement with Self-Feedback

2023 · arXiv (Cornell University)

Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from …