14 papers
Rethinking Fairness in LLM-Based Recommender Systems: A Survey
Song-Duo Ma, Chu-Yun Chen, Bang-An Li +3
Large Language Models (LLMs) are reshaping recommender systems by enabling more semantic, generative, and interactive recommendation pipelines. However, this shift also introduces…
RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Adaptive LLM-Generated Fake News Detection
Song-Duo Ma, Yi-Hung Liu, Hsin-Yu Lin +4
To efficiently combat the spread of LLM-generated misinformation in the news domain, we present RADAR, a Retrieval-Augmented Detector with Adversarial Refinement for adaptive LLM-g…
The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search
Rongzhe Wei, Peizhi Niu, Xinjie Shen +7
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Existing approaches overwhelmingly operate within the p…
Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models
Huzaifa Arif, Pin-Yu Chen, Alex Gittens +2
With the increasing reliance on AI models for weather forecasting, it is imperative to evaluate their vulnerability to adversarial perturbations. This work introduces Weather Adapt…
Adversarial Attack-Defense Co-Evolution for LLM Safety Alignment via Tree-Group Dual-Aware Search and Optimization
Xurui Li, Kaisong Song, Rui Zhu +2
Large Language Models (LLMs) have developed rapidly in web services, delivering unprecedented capabilities while amplifying societal risks. Existing works tend to focus on either i…
ICX360: In-Context eXplainability 360 Toolkit
Dennis Wei, Ronny Luss, Xiaomeng Hu +6
Large Language Models (LLMs) have become ubiquitous in everyday life and are entering higher-stakes applications ranging from summarizing meeting transcripts to answering doctors'…
Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion
Sean Patten, Pin-Yu Chen, Christina Schweikert +1
This paper presents a novel approach to sentiment classification using the application of Combinatorial Fusion Analysis (CFA) to integrate an ensemble of diverse machine learning m…
CarBoN: Calibrated Best-of-N Sampling Improves Test-time Reasoning
Yung-Chen Tang, Pin-Yu Chen, Andrea Cavallaro
Allocating more computation during inference time (test-time scaling) improves language model performance, especially for reasoning tasks. However, popular methods like Best-of-…
Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction
Zesheng Ye, Chengyi Cai, Ruijiang Dong +4
As large-scale pre-trained foundation models continue to expand in size and capability, efficiently adapting them to specific downstream tasks has become increasingly critical. Des…
Group-Adaptive Threshold Optimization for Robust AI-Generated Text Detection
Minseok Jung, Cynthia Fuertes Panizo, Liam Dugan +4
The advancement of large language models (LLMs) has made it difficult to differentiate human-written text from AI-generated text. Several AI-text detectors have been developed in r…
Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering
Youngsun Lim, Hojun Choi, Hyunjung Shim
Despite the impressive success of text-to-image (TTI) generation models, existing studies overlook the issue of whether these models accurately convey factual information. In this…
Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models
Shashank Kotyan, Pin-Yu Chen, Danilo Vasconcellos Vargas
Most evaluations of vision models use indirect methods to assess latent space quality. These methods often involve adding extra layers to project the latent space into a new one. T…
Improving Transformers using Faithful Positional Encoding
Tsuyoshi Idé, Jokin Labaien, Pin-Yu Chen
We propose a new positional encoding method for a neural network architecture called the Transformer. Unlike the standard sinusoidal positional encoding, our approach is based on s…
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Ming Jin, Shiyu Wang, Lintao Ma +8
Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision…