15 citations · 25 across the 8 of their papers we have counts for
8 papers
Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond
Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin
Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this ar…
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
Jun Yan, Vikas Yadav, Shiyang Li +6
Instruction-tuned Large Language Models (LLMs) have become a ubiquitous platform for open-ended applications due to their ability to modulate responses based on human instructions.…
Instruction-following Evaluation through Verbalizer Manipulation
Shiyang Li, Jun Yan, Hai Wang +4
While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challe…
AlpaGasus: Training A Better Alpaca with Fewer Data
Lichang Chen, Shiyang Li, Jun Yan +8
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT data…
A Robust Semantic Frame Parsing Pipeline on a New Complex Twitter Dataset
Yu Wang, Hongxia Jin
Most recent semantic frame parsing systems for spoken language understanding (SLU) are designed based on recurrent neural networks. These systems display decent performance on benc…
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling
Kalpa Gunaratna, Vijay Srinivasan, Akhila Yerukola +1
Joint intent detection and slot filling is a key research topic in natural language understanding (NLU). Existing joint intent and slot filling systems analyze and compute features…
ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs
Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan +1
Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and sati…
Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phones
Kalpa Gunaratna, Vijay Srinivasan, Sandeep Nama +1
Information Extraction from visual documents enables convenient and intelligent assistance to end users. We present a Neighborhood-based Information Extraction (NIE) approach that…