22 citations · 50 across the 10 of their papers we have counts for
12 papers
Counterspeech for Mitigating the Influence of Media Bias: Comparing Human and LLM-Generated Responses
Luyang Lin, Zijin Feng, Lingzhi Wang +1
Biased news contributes to societal polarization and is often reinforced by hostile reader comments, constituting a vital yet often overlooked aspect of news dissemination. Our stu…
Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception
Luyang Lin, Lingzhi Wang, Jinsong Guo +1
The pervasive spread of misinformation and disinformation in social media underscores the critical importance of detecting media bias. While robust Large Language Models (LLMs) hav…
IndiTag: An Online Media Bias Analysis System Using Fine-Grained Bias Indicators
Luyang Lin, Lingzhi Wang, Jinsong Guo +2
In the age of information overload and polarized discourse, understanding media bias has become imperative for informed decision-making and fostering a balanced public discourse. H…
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models
Lingzhi Wang, Xingshan Zeng, Jinsong Guo +2
This paper explores Machine Unlearning (MU), an emerging field that is gaining increased attention due to concerns about neural models unintentionally remembering personal or sensi…
IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators
Luyang Lin, Lingzhi Wang, Xiaoyan Zhao +2
This study focuses on media bias detection, crucial in today's era of influential social media platforms shaping individual attitudes and opinions. In contrast to prior work that p…
A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Models
Hongru Wang, Lingzhi Wang, Yiming Du +4
Dialogue systems (DS), including the task-oriented dialogue system (TOD) and the open-domain dialogue system (ODD), have always been a fundamental task in natural language processi…
TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration
Hongru Wang, Huimin Wang, Lingzhi Wang +6
Large language models (LLMs) have demonstrated exceptional performance in planning the use of various functional tools, such as calculators and retrievers, particularly in question…
Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices
Bojia Zi, Xianbiao Qi, Lingzhi Wang +3
In this paper, we present Delta-LoRA, which is a novel parameter-efficient approach to fine-tune large language models (LLMs). In contrast to LoRA and other low-rank adaptation met…
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Lingzhi Wang, Tong Chen, Wei Yuan +3
Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about…
Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation
Lingzhi Wang, Mrinmaya Sachan, Xingshan Zeng +1
Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog. CTSs have become a key pillar in…
Opportunities and Challenges in Neural Dialog Tutoring
Jakub Macina, Nico Daheim, Lingzhi Wang +4
Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors. Although there have been significant r…
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models
Lingzhi Wang, Huang Hu, Lei Sha +3
Conversational Recommender System (CRS), which aims to recommend high-quality items to users through interactive conversations, has gained great research interest recently. A CRS i…