activity
20202026
most citedAI vs. Human -- Differentiation Analysis of Scientific Content Generation

76 citations · 94 across the 16 of their papers we have counts for

collaborators

16 papers

cs.IR2026

From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper Evaluation

Pujun Zheng, Jiacheng Yao, Jinquan Zheng +6

Large language models (LLMs) are currently applied to scientific paper evaluation by assigning an absolute score to each paper independently. However, since score scales vary acros…

cs.CL2025★ 2 cited

Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation

Jiahao Cheng, Tiancheng Su, Jia Yuan +5

Large Language Models (LLMs) often exhibit \textit{hallucinations}, generating factually incorrect or semantically irrelevant content in response to prompts. Chain-of-Thought (CoT)…

cs.LG2025

Modality Equilibrium Matters: Minor-Modality-Aware Adaptive Alternating for Cross-Modal Memory Enhancement

Xiang Shi, Rui Zhang, Jiawei Liu +3

Multimodal fusion is susceptible to modality imbalance, where dominant modalities overshadow weak ones, easily leading to biased learning and suboptimal fusion, especially for inco…

cs.CL2024

Interweaving Memories of a Siamese Large Language Model

Xin Song, Zhikai Xue, Guoxiu He +2

Parameter-efficient fine-tuning (PEFT) methods optimize large language models (LLMs) by modifying or introducing a small number of parameters to enhance alignment with downstream t…

cs.CL2024

A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis

Kaisong Song, Yangyang Kang, Jiawei Liu +3

User Satisfaction Estimation is an important task and increasingly being applied in goal-oriented dialogue systems to estimate whether the user is satisfied with the service. It is…

cs.CR2024

PersonaMark: Personalized LLM watermarking for model protection and user attribution

Yuehan Zhang, Peizhuo Lv, Yinpeng Liu +5

The rapid advancement of customized Large Language Models (LLMs) offers considerable convenience. However, it also intensifies concerns regarding the protection of copyright/confid…

cs.CL2024

Every Part Matters: Integrity Verification of Scientific Figures Based on Multimodal Large Language Models

Xiang Shi, Jiawei Liu, Yinpeng Liu +2

This paper tackles a key issue in the interpretation of scientific figures: the fine-grained alignment of text and figures. It advances beyond prior research that primarily dealt w…

cs.CL2024

Enhance Robustness of Language Models Against Variation Attack through Graph Integration

Zi Xiong, Lizhi Qing, Yangyang Kang +5

The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models' vulnerability to adv…

cs.CL2024

From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications

Yongqiang Ma, Lizhi Qing, Jiawei Liu +5

Evaluating large language models (LLMs) is fundamental, particularly in the context of practical applications. Conventional evaluation methods, typically designed primarily for LLM…

cs.CL2024★ 4 cited

Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning

Yinpeng Liu, Jiawei Liu, Xiang Shi +3

Demonstration ordering, which is an important strategy for in-context learning (ICL), can significantly affects the performance of large language models (LLMs). However, most of th…

cs.IR2023

Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher

Xiang Shi, Jiawei Liu, Yinpeng Liu +2

The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches. However, challenges arise in validating the reli…

cs.CL2023★ 3 cited

Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge

Jiawei Liu, Zi Xiong, Yi Jiang +4

Fine-tuning pre-trained language models (PLMs), e.g., SciBERT, generally requires large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in…

cs.CL2023★ 76 cited

AI vs. Human -- Differentiation Analysis of Scientific Content Generation

Yongqiang Ma, Jiawei Liu, Fan Yi +4

Recent neural language models have taken a significant step forward in producing remarkably controllable, fluent, and grammatical text. Although studies have found that AI-generate…

cs.IR2022★ 7 cited

Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models

Jiawei Liu, Yangyang Kang, Di Tang +5

Neural text ranking models have witnessed significant advancement and are increasingly being deployed in practice. Unfortunately, they also inherit adversarial vulnerabilities of g…

cs.CL2021

A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis

Jiawei Liu, Kaisong Song, Yangyang Kang +5

Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital appreh…

cs.CL2020★ 2 cited

Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff

Jiawei Liu, Zhe Gao, Yangyang Kang +5

Is chatbot able to completely replace the human agent? The short answer could be - "it depends...". For some challenging cases, e.g., dialogue's topical spectrum spreads beyond the…