3 citations · 9 across the 18 of their papers we have counts for
19 papers
Analyzing Bias in False Refusal Behavior of Large Language Models for Hate Speech Detoxification
Kyuri Im, Shuzhou Yuan, Michael Färber
While large language models (LLMs) have increasingly been applied to hate speech detoxification, the prompts often trigger safety alerts, causing LLMs to refuse the task. In this s…
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
Hengyuan Zhang, Zhihao Zhang, Mingyang Wang +26
Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat…
The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Konrad Löhr, Shuzhou Yuan, Michael Färber
Large Language Models (LLMs) are increasingly integral to information dissemination and decision-making processes. Given their growing societal influence, understanding potential b…
Beyond Over-Refusal: Scenario-Based Diagnostics and Post-Hoc Mitigation for Exaggerated Refusals in LLMs
Shuzhou Yuan, Ercong Nie, Yinuo Sun +3
Large language models (LLMs) frequently produce false refusals, declining benign requests that contain terms resembling unsafe queries. We address this challenge by introducing two…
CoDAE: Adapting Large Language Models for Education via Chain-of-Thought Data Augmentation
Shuzhou Yuan, William LaCroix, Hardik Ghoshal +2
Large Language Models (LLMs) are increasingly employed as AI tutors due to their scalability and potential for personalized instruction. However, off-the-shelf LLMs often underperf…
From Monolingual to Bilingual: Investigating Language Conditioning in Large Language Models for Psycholinguistic Tasks
Shuzhou Yuan, Zhan Qu, Mario Tawfelis +1
Large Language Models (LLMs) exhibit strong linguistic capabilities, but little is known about how they encode psycholinguistic knowledge across languages. We investigate whether a…
PoeTone: A Framework for Constrained Generation of Structured Chinese Songci with LLMs
Zhan Qu, Shuzhou Yuan, Michael Färber
This paper presents a systematic investigation into the constrained generation capabilities of large language models (LLMs) in producing Songci, a classical Chinese poetry form cha…
Hateful Person or Hateful Model? Investigating the Role of Personas in Hate Speech Detection by Large Language Models
Shuzhou Yuan, Ercong Nie, Mario Tawfelis +3
Hate speech detection is a socially sensitive and inherently subjective task, with judgments often varying based on personal traits. While prior work has examined how socio-demogra…
LLM in the Loop: Creating the ParaDeHate Dataset for Hate Speech Detoxification
Shuzhou Yuan, Ercong Nie, Lukas Kouba +4
Detoxification, the task of rewriting harmful language into non-toxic text, has become increasingly important amid the growing prevalence of toxic content online. However, high-qua…
Explainable LiDAR 3D Point Cloud Segmentation and Clustering for Detecting Airplane-Generated Wind Turbulence
Zhan Qu, Shuzhou Yuan, Michael Färber +3
Wake vortices - strong, coherent air turbulences created by aircraft - pose a significant risk to aviation safety and therefore require accurate and reliable detection methods. In…
Can Hallucinations Help? Boosting LLMs for Drug Discovery
Shuzhou Yuan, Zhan Qu, Ashish Yashwanth Kangen +1
Hallucinations in large language models (LLMs), plausible but factually inaccurate text, are often viewed as undesirable. However, recent work suggests that such outputs may hold c…
Graph-Guided Textual Explanation Generation Framework
Shuzhou Yuan, Jingyi Sun, Ran Zhang +4
Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model's reasoning about its predictions. However, recent work has questioned…
GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism
Shuzhou Yuan, Michael Färber
Pretrained Language Models (PLMs) benefit from external knowledge stored in graph structures for various downstream tasks. However, bridging the modality gap between graph structur…
Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models
Ercong Nie, Shuzhou Yuan, Bolei Ma +4
Probing the multilingual knowledge of linguistic structure in LLMs, often characterized as sequence labeling, faces challenges with maintaining output templates in current text-to-…
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network
Shuzhou Yuan, Ercong Nie, Michael Färber +2
Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are used. However, fine-tuning still remains crucial to further…
Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers
Shuzhou Yuan, Ercong Nie, Bolei Ma +1
Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks. However, the sheer size of these models poses challenge…
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
Bolei Ma, Ercong Nie, Shuzhou Yuan +4
Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily f…
Evaluating Generative Models for Graph-to-Text Generation
Shuzhou Yuan, Michael Färber
Large language models (LLMs) have been widely employed for graph-to-text generation tasks. However, the process of finetuning LLMs requires significant training resources and annot…
Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation
Michael Färber, Melissa Coutinho, Shuzhou Yuan
With the remarkable increase in the number of scientific entities such as publications, researchers, and scientific topics, and the associated information overload in science, acad…