97 citations · 200 across the 11 of their papers we have counts for
11 papers · 1 filter
Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification
Haochun Wang, Sendong Zhao, Chi Liu +4
Prompt-based classification adapts tasks to a cloze question format utilizing the [MASK] token and the filled tokens are then mapped to labels through pre-defined verbalizers. Rece…
Global Prompt Cell: A Portable Control Module for Effective Prompt Tuning
Chi Liu, Haochun Wang, Nuwa Xi +2
As a novel approach to tuning pre-trained models, prompt tuning involves freezing the parameters in downstream tasks while inserting trainable embeddings into inputs in the first l…
HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge
Haochun Wang, Chi Liu, Nuwa Xi +4
Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks. Nevertheless, LLMs h…
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
Haochun Wang, Chi Liu, Nuwa Xi +7
Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prom…
Structured Prompt Tuning
Chi-Liang Liu, Hung-yi Lee, Wen-tau Yih
We propose structured prompt tuning, a simple and effective method to improve prompt tuning. Instead of prepending a sequence of tunable embeddings to the input, we generate the so…
What makes multilingual BERT multilingual?
Chi-Liang Liu, Tsung-Yuan Hsu, Yung-Sung Chuang +1
Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings. In this work, we provide an in-depth exper…
Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
Chi-Liang Liu, Hung-yi Lee
In this paper, we study the possibility of almost unsupervised Multiple Choices Question Answering (MCQA). Starting from very basic knowledge, MCQA model knows that some choices ha…
A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT
Chi-Liang Liu, Tsung-Yuan Hsu, Yung-Sung Chuang +1
Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings. In this work, we provide an in-depth exper…
SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering
Yung-Sung Chuang, Chi-Liang Liu, Hung-Yi Lee +1
While various end-to-end models for spoken language understanding tasks have been explored recently, this paper is probably the first known attempt to challenge the very difficult…
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model
Tsung-yuan Hsu, Chi-liang Liu, Hung-yi Lee
Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zer…
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension
Chia-Hsuan Li, Szu-Lin Wu, Chi-Liang Liu +1
Reading comprehension has been widely studied. One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is alread…