Search Results for author:
Found 7 papers, 3 papers with code
Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond
Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy.
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
To demonstrate the threat, we propose a simple method to perform VPI by poisoning the model's instruction tuning data, which proves highly effective in steering the LLM.
Instruction-following Evaluation through Verbalizer Manipulation
We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them.
AlpaGasus: Training A Better Alpaca with Fewer Data
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data.
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling
In this work, we propose a novel approach that: (i) learns to generate additional slot type specific features in order to improve accuracy and (ii) provides explanations for slot filling decisions for the first time in a joint NLU model.
ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs
To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query.
Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phones
Information Extraction from visual documents enables convenient and intelligent assistance to end users.
