20 citations · 33 across the 15 of their papers we have counts for
15 papers
STAMPsy: Towards SpatioTemporal-Aware Mixed-Type Dialogues for Psychological Counseling
Jieyi Wang, Yue Huang, Zeming Liu +7
Online psychological counseling dialogue systems are trending, offering a convenient and accessible alternative to traditional in-person therapy. However, existing psychological co…
ReFF: Reinforcing Format Faithfulness in Language Models across Varied Tasks
Jiashu Yao, Heyan Huang, Zeming Liu +4
Following formatting instructions to generate well-structured content is a fundamental yet often unmet capability for large language models (LLMs). To study this capability, which…
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
Boyang Xue, Hongru Wang, Rui Wang +5
The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trus…
FAME: Towards Factual Multi-Task Model Editing
Li Zeng, Yingyu Shan, Zeming Liu +2
Large language models (LLMs) embed extensive knowledge and utilize it to perform exceptionally well across various tasks. Nevertheless, outdated knowledge or factual errors within…
A Survey on Data Synthesis and Augmentation for Large Language Models
Ke Wang, Jiahui Zhu, Minjie Ren +8
The success of Large Language Models (LLMs) is inherently linked to the availability of vast, diverse, and high-quality data for training and evaluation. However, the growth rate o…
AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction
Hongru Wang, Rui Wang, Boyang Xue +5
Large Language Models (LLMs) can interact with the real world by connecting with versatile external APIs, resulting in better problem-solving and task automation capabilities. Prev…
Deterministic Reversible Data Augmentation for Neural Machine Translation
Jiashu Yao, Heyan Huang, Zeming Liu +1
Data augmentation is an effective way to diversify corpora in machine translation, but previous methods may introduce semantic inconsistency between original and augmented data bec…
Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges
Xiaoming Shi, Zeming Liu, Li Du +6
This paper surveys and organizes research works on medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical comm…
2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion
Dongsheng Wang, Xiaoqin Feng, Zeming Liu +1
Named entity recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying entities in sentences into pre-defined types. It plays…
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
Boyang Xue, Hongru Wang, Rui Wang +6
The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trus…
Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation
Can Chen, Hao Liu, Zeming Liu +2
Proactively and naturally guiding the dialog from the non-recommendation context (e.g., Chit-chat) to the recommendation scenario (e.g., Music) is crucial for the Conversational Re…
MidMed: Towards Mixed-Type Dialogues for Medical Consultation
Xiaoming Shi, Zeming Liu, Chuan Wang +4
Most medical dialogue systems assume that patients have clear goals (medicine querying, surgical operation querying, etc.) before medical consultation. However, in many real scenar…
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals
Zeming Liu, Jun Xu, Zeyang Lei +3
Most dialog systems posit that users have figured out clear and specific goals before starting an interaction. For example, users have determined the departure, the destination, an…
DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation
Zeming Liu, Haifeng Wang, Zheng-Yu Niu +2
In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and…
Towards Conversational Recommendation over Multi-Type Dialogs
Zeming Liu, Haifeng Wang, Zheng-Yu Niu +3
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e…