5 citations · 16 across the 21 of their papers we have counts for
15 papers · 1 filter
NEAT: Neuron-Based Early Exit for Large Reasoning Models
Kang Liu, Yongkang Liu, Xiaocui Yang +5
Large Reasoning Models (LRMs) often suffer from \emph{overthinking}, a phenomenon in which redundant reasoning steps are generated after a correct solution has already been reached…
PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs
Zijing Wang, Yongkang Liu, Mingyang Wang +8
Multimodal Large Language Models (MLLMs) rely on strong linguistic reasoning inherited from their base language models. However, multimodal instruction fine-tuning paradoxically de…
High-Rank Structured Modulation for Parameter-Efficient Fine-Tuning
Yongkang Liu, Xing Li, Mengjie Zhao +7
As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large language models. Low-rank Adaptation…
SAD: A Large-Scale Strategic Argumentative Dialogue Dataset
Yongkang Liu, Jiayang Yu, Mingyang Wang +6
Argumentation generation has attracted substantial research interest due to its central role in human reasoning and decision-making. However, most existing argumentative corpora fo…
MEKiT: Multi-source Heterogeneous Knowledge Injection Method via Instruction Tuning for Emotion-Cause Pair Extraction
Shiyi Mu, Yongkang Liu, Shi Feng +3
Although large language models (LLMs) excel in text comprehension and generation, their performance on the Emotion-Cause Pair Extraction (ECPE) task, which requires reasoning abili…
SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model
Jiayang Yu, Yihang Zhang, Bin Wang +3
Fine-tuning is a key approach for adapting language models to specific downstream tasks, but updating all model parameters becomes impractical as model sizes increase. Parameter-Ef…
ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language
Yongkang Liu, Feng Shi, Daling Wang +2
Although large language models(LLMs) show amazing capabilities, among various exciting applications discovered for LLMs fall short in other low-resource languages. Besides, most ex…
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
Yongkang Liu, Ercong Nie, Shi Feng +5
Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient…
Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response
Yongkang Liu, Shi Feng, Daling Wang +2
LLMs (large language models) such as ChatGPT have shown remarkable language understanding and generation capabilities. Although reference-free evaluators based on LLMs show better…
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism
Yongkang Liu, Shi Feng, Daling Wang +2
We investigate response generation for multi-turn dialogue in generative-based chatbots. Existing generative models based on RNNs (Recurrent Neural Networks) usually employ the las…
DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection
Yongkang Liu, Shi Feng, Wei Gao +2
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms. Although promising results have been ac…
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
Yongkang Liu, Shi Feng, Daling Wang +1
Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre…
An Understanding-Oriented Robust Machine Reading Comprehension Model
Feiliang Ren, Yongkang Liu, Bochao Li +5
Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this paper, we propose an understanding-oriented mac…
Deep Understanding based Multi-Document Machine Reading Comprehension
Feiliang Ren, Yongkang Liu, Bochao Li +7
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two…
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training
Yongkang Liu, Shi Feng, Daling Wang +3
We investigate response selection for multi-turn conversation in retrieval-based chatbots. Existing studies pay more attention to the matching between utterances and responses by c…