no code implementations • 25 Sep 2023 • Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin
Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy.
1 code implementation • 31 Jul 2023 • Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin
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.
no code implementations • 20 Jul 2023 • Shiyang Li, Jun Yan, Hai Wang, Zheng Tang, Xiang Ren, Vijay Srinivasan, Hongxia Jin
We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them.
3 code implementations • 17 Jul 2023 • Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data.
no code implementations • 19 Oct 2022 • Kalpa Gunaratna, Vijay Srinivasan, Akhila Yerukola, Hongxia Jin
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.
1 code implementation • 13 Dec 2021 • Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin
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.
no code implementations • 23 Aug 2021 • Kalpa Gunaratna, Vijay Srinivasan, Sandeep Nama, Hongxia Jin
Information Extraction from visual documents enables convenient and intelligent assistance to end users.