148 citations · 416 across the 27 of their papers we have counts for
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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…