148 citations · 403 across the 25 of their papers we have counts for
17 papers · 1 filter
KaFT: Knowledge-aware Fine-tuning for Boosting LLMs' Domain-specific Question-Answering Performance
Qihuang Zhong, Liang Ding, Xiantao Cai +3
Supervised fine-tuning (SFT) is a common approach to improve the domain-specific question-answering (QA) performance of large language models (LLMs). However, recent literature rev…
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL
Qihuang Zhong, Kunfeng Chen, Liang Ding +3
Large Language Models (LLMs) have shown promising performance in text-to-SQL, which involves translating natural language questions into SQL queries. However, current text-to-SQL L…
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis
Qihuang Zhong, Haiyun Li, Luyao Zhuang +2
Aspect-based Sentiment Analysis (ABSA) is an important sentiment analysis task, which aims to determine the sentiment polarity towards an aspect in a sentence. Due to the expensive…
Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Qihuang Zhong, Kang Wang, Ziyang Xu +3
Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex…
Revisiting Knowledge Distillation for Autoregressive Language Models
Qihuang Zhong, Liang Ding, Li Shen +3
Knowledge distillation (KD) is a common approach to compress a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, in the…
ROSE Doesn't Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding
Qihuang Zhong, Liang Ding, Juhua Liu +2
With the development of instruction-tuned large language models (LLMs), improving the safety of LLMs has become more critical. However, the current approaches for aligning the LLMs…
Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models
Miaoxi Zhu, Qihuang Zhong, Li Shen +4
Quantization is a promising approach for reducing memory overhead and accelerating inference, especially in large pre-trained language model (PLM) scenarios. While having no access…
Self-Evolution Learning for Discriminative Language Model Pretraining
Qihuang Zhong, Liang Ding, Juhua Liu +2
Masked language modeling, widely used in discriminative language model (e.g., BERT) pretraining, commonly adopts a random masking strategy. However, random masking does not conside…
Revisiting Token Dropping Strategy in Efficient BERT Pretraining
Qihuang Zhong, Liang Ding, Juhua Liu +4
Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at s…
Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT
Qihuang Zhong, Liang Ding, Juhua Liu +2
Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. Several prior studies have shown that ChatGPT attains rema…
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE
Qihuang Zhong, Liang Ding, Keqin Peng +5
This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of…
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
Qihuang Zhong, Liang Ding, Yibing Zhan +11
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language…
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models
Qihuang Zhong, Liang Ding, Li Shen +4
Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization. Prior works show that the recently-proposed sharpness-aware mini…
PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation
Qihuang Zhong, Liang Ding, Juhua Liu +2
Prompt Transfer (PoT) is a recently-proposed approach to improve prompt-tuning, by initializing the target prompt with the existing prompt trained on similar source tasks. However,…
E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation
Qihuang Zhong, Liang Ding, Juhua Liu +2
Sequence-to-sequence (seq2seq) learning is a popular fashion for large-scale pretraining language models. However, the prior seq2seq pretraining models generally focus on reconstru…
Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis
Qihuang Zhong, Liang Ding, Juhua Liu +3
Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information…
Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis
Juhua Liu, Qihuang Zhong, Liang Ding +3
Aspect-based Sentiment Analysis (ABSA) aims to determine the sentiment polarity towards an aspect. Because of the expensive and limited labelled data, the pretraining strategy has…