70 citations · 192 across the 16 of their papers we have counts for
11 papers · 1 filter
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs
Runchu Tian, Yanghao Li, Yuepeng Fu +10
Positional bias in large language models (LLMs) hinders their ability to effectively process long inputs. A prominent example is the "lost in the middle" phenomenon, where LLMs str…
Won't Get Fooled Again: Answering Questions with False Premises
Shengding Hu, Yifan Luo, Huadong Wang +3
Pre-trained language models (PLMs) have shown unprecedented potential in various fields, especially as the backbones for question-answering (QA) systems. However, they tend to be e…
Exploring Format Consistency for Instruction Tuning
Shihao Liang, Runchu Tian, Kunlun Zhu +6
Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions. It is shown that increasing the diversity and number of i…
WebCPM: Interactive Web Search for Chinese Long-form Question Answering
Yujia Qin, Zihan Cai, Dian Jin +12
Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two proced…
Recyclable Tuning for Continual Pre-training
Yujia Qin, Cheng Qian, Xu Han +6
Continual pre-training is the paradigm where pre-trained language models (PLMs) continually acquire fresh knowledge from growing data and gradually get upgraded. Before an upgraded…
Plug-and-Play Knowledge Injection for Pre-trained Language Models
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin +8
Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. However, massive retraining is required to deploy ne…
Tool Learning with Foundation Models
Yujia Qin, Shengding Hu, Yankai Lin +38
Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models,…
FPT: Improving Prompt Tuning Efficiency via Progressive Training
Yufei Huang, Yujia Qin, Huadong Wang +4
Recently, prompt tuning (PT) has gained increasing attention as a parameter-efficient way of tuning pre-trained language models (PLMs). Despite extensively reducing the number of t…
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
Yuan Yao, Qingxiu Dong, Jian Guan +32
Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding ro…
On Transferability of Prompt Tuning for Natural Language Processing
Yusheng Su, Xiaozhi Wang, Yujia Qin +10
Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-paramet…
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification
Shengding Hu, Ning Ding, Huadong Wang +5
Tuning pre-trained language models (PLMs) with task-specific prompts has been a promising approach for text classification. Particularly, previous studies suggest that prompt-tunin…