183 citations · 2.2k across the 143 of their papers we have counts for
143 papers
Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond
Siyang Liu, Naihao Deng, Sahand Sabour +3
We propose task-adaptive tokenization as a way to adapt the generation pipeline to the specifics of a downstream task and enhance long-form generation in mental health. Inspired by…
SafetyBench: Evaluating the Safety of Large Language Models
Zhexin Zhang, Leqi Lei, Lindong Wu +7
With the rapid development of Large Language Models (LLMs), increasing attention has been paid to their safety concerns. Consequently, evaluating the safety of LLMs has become an e…
Large Language Models Are Not Robust Multiple Choice Selectors
Chujie Zheng, Hao Zhou, Fandong Meng +2
Multiple choice questions (MCQs) serve as a common yet important task format in the evaluation of large language models (LLMs). This work shows that modern LLMs are vulnerable to o…
AgentBench: Evaluating LLMs as Agents
Xiao Liu, Hao Yu, Hanchen Zhang +19
The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on cha…
DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering
Pei Ke, Fei Huang, Fei Mi +4
Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. Specifically, most of the well-performed…
Mitigating the Learning Bias towards Repetition by Self-Contrastive Training for Open-Ended Generation
Jian Guan, Minlie Huang
Despite the huge progress in myriad generation tasks, pretrained language models (LMs) such as GPT2 still tend to generate repetitive texts with maximization-based decoding algorit…
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning
Chujie Zheng, Pei Ke, Zheng Zhang +1
It has always been an important yet challenging problem to control language models to avoid generating texts with undesirable attributes, such as toxic language and unnatural repet…
Uncertainty in Natural Language Processing: Sources, Quantification, and Applications
Mengting Hu, Zhen Zhang, Shiwan Zhao +2
As a main field of artificial intelligence, natural language processing (NLP) has achieved remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in a…
MiniLLM: On-Policy Distillation of Large Language Models
Yuxian Gu, Li Dong, Furu Wei +1
Knowledge Distillation (KD) is a promising technique for reducing the high computational demand of large language models (LLMs). However, previous KD methods are primarily applied…
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Zhen Zhang, Mengting Hu, Shiwan Zhao +6
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER syste…
Pre-Training to Learn in Context
Yuxian Gu, Li Dong, Furu Wei +1
In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP communi…
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
Zhihong Shao, Yeyun Gong, Yelong Shen +3
Large language models are powerful text processors and reasoners, but are still subject to limitations including outdated knowledge and hallucinations, which necessitates connectin…
Directed Acyclic Transformer Pre-training for High-quality Non-autoregressive Text Generation
Fei Huang, Pei Ke, Minlie Huang
Non-AutoRegressive (NAR) text generation models have drawn much attention because of their significantly faster decoding speed and good generation quality in machine translation. H…
Tailoring Language Generation Models under Total Variation Distance
Haozhe Ji, Pei Ke, Zhipeng Hu +2
The standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method. From a distributional view, MLE in fact minimizes the Kullb…
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
Zhihong Shao, Yeyun Gong, Yelong Shen +3
Large language models can perform various reasoning tasks by using chain-of-thought prompting, which guides them to find answers through step-by-step demonstrations. However, the q…
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation
Zhexin Zhang, Jiale Cheng, Hao Sun +5
Large pretrained language models can easily produce toxic or biased content, which is prohibitive for practical use. In order to detect such toxic generations, existing methods rel…
KPT: Keyword-guided Pre-training for Grounded Dialog Generation
Qi Zhu, Fei Mi, Zheng Zhang +6
Incorporating external knowledge into the response generation process is essential to building more helpful and reliable dialog agents. However, collecting knowledge-grounded conve…
Chaining Simultaneous Thoughts for Numerical Reasoning
Zhihong Shao, Fei Huang, Minlie Huang
Given that rich information is hidden behind ubiquitous numbers in text, numerical reasoning over text should be an essential skill of AI systems. To derive precise equations to so…
AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning
Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang +2
Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizabili…
Aligning Recommendation and Conversation via Dual Imitation
Jinfeng Zhou, Bo Wang, Minlie Huang +4
Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations…
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
Qi Zhu, Christian Geishauser, Hsien-chin Lin +10
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants. Existing toolkits for build…
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization
Yuxian Gu, Pei Ke, Xiaoyan Zhu +1
Training language models to learn from human instructions for zero-shot cross-task generalization has attracted much attention in NLP communities. Recently, instruction tuning (IT)…
CDConv: A Benchmark for Contradiction Detection in Chinese Conversations
Chujie Zheng, Jinfeng Zhou, Yinhe Zheng +6
Dialogue contradiction is a critical issue in open-domain dialogue systems. The contextualization nature of conversations makes dialogue contradiction detection rather challenging.…
Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China
Sahand Sabour, Wen Zhang, Xiyao Xiao +5
The growing demand for mental health support has highlighted the importance of conversational agents as human supporters worldwide and in China. These agents could increase availab…