204 citations · 276 across the 17 of their papers we have counts for
6 papers · 1 filter
How Does Beam Search improve Span-Level Confidence Estimation in Generative Sequence Labeling?
Kazuma Hashimoto, Iftekhar Naim, Karthik Raman
Sequence labeling is a core task in text understanding for IE/IR systems. Text generation models have increasingly become the go-to solution for such tasks (e.g., entity extraction…
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation
Krishna Srinivasan, Karthik Raman, Anupam Samanta +3
Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks. Search queries though pose a unique challenge, given their short-length and lac…
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation
Sebastian Hofstätter, Jiecao Chen, Karthik Raman +1
Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an…
GROOT: Corrective Reward Optimization for Generative Sequential Labeling
Kazuma Hashimoto, Karthik Raman
Sequential labeling is a fundamental NLP task, forming the backbone of many applications. Supervised learning of Seq2Seq models has shown great success on these problems. However,…
Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling
Sebastian Hofstätter, Jiecao Chen, Karthik Raman +1
This paper studies multi-task training of retrieval-augmented generation models for knowledge-intensive tasks. We propose to clean the training set by utilizing a distinct property…
Transforming Sequence Tagging Into A Seq2Seq Task
Karthik Raman, Iftekhar Naim, Jiecao Chen +3
Pretrained, large, generative language models (LMs) have had great success in a wide range of sequence tagging and structured prediction tasks. Casting a sequence tagging task as a…