32 citations · 202 across the 82 of their papers we have counts for
14 papers · 1 filter
Personalization Matters: Long-Horizon Conversation Agent with User-Centric Information in Online Shopping Interactions
Rena Gao, Yue Dai, Hao Guan +4
Personalized conversational shopping requires maintaining preference consistency over multi-turn interactions, where users reveal constraints gradually. Existing approaches often r…
OLMo-Detect: A Multi-Stage, Confounder-Controlled Benchmark for Membership Inference on Large Language Models
Tao Shi, Chaoyi Xiang, Qiongkai Xu +1
Membership inference on large language models (LLMs) aims to determine whether a given text sample was included in an LLM's training data, without access to its training corpus. De…
Enhancing Assessment of Self-Consistency in LLM Explanations using Perturbation Strength
Phuong Q. Le, Kemal Kurniawan, Jey Han Lau
Prior work has examined the self-consistency of LLM-generated explanations using surface-level perturbation methods. However, the strength of these perturbations is not explicitly…
AutoLexSteer: Automatic Contrast Construction for Lexical Activation Steering
Shuhe Wang, Lachlan Cowley, Eduard Hovy +1
Steering vectors have rapidly emerged as a popular and effective method for guiding the output of LLMs in very specific ways. But constructing accurate steering vectors is a diffic…
MultiGhostBench: A Multilingual Benchmark for Long-Form LLM-Generated Text Attribution under Distribution Shifts
Matteo Greco, Anudeex Shetty, Andrea Tagarelli +1
While existing work on LLM authorship attribution (AA) has made progress, available benchmarks remain limited, often focusing on English, controlled settings, or relatively outdate…
Reward on Path: Learning Intermediate Supervision Signals for Knowledge Graph Question Answering
Shengxiang Gao, Chao Lei, Jey Han Lau +2
Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs). Recent methods use supervision derived from answer labels or refin…
Beyond Long Tail POIs: Transition-Centered Generalization for Human Mobility Prediction
Dingyang Lyu, Zhengjia Xu, Jey Han Lau +1
Human mobility prediction forecasts a user's next Point of Interest (POI) from historical trajectories, supporting applications from recommendation to urban planning. Recent studie…
CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics
Ming-Bin Chen, Jey Han Lau, Lea Frermann
Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conversation. We introduce a fram…
Controlling Distributional Bias in Multi-Round LLM Generation via KL-Optimized Fine-Tuning
Yanbei Jiang, Amr Keleg, Ryandito Diandaru +4
While the real world is inherently stochastic, Large Language Models (LLMs) are predominantly evaluated on single-round inference against fixed ground truths. In this work, we shif…
Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space
Mohammad Reza Ghasemi Madani, Soyeon Caren Han, Shuo Yang +1
Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs). However, LLMs remain vulnerable to the presence of plausible distractors. This often dive…
Who Wrote the Book? Detecting and Attributing LLM Ghostwriters
Anudeex Shetty, Qiongkai Xu, Olga Ohrimenko +1
In this paper, we introduce GhostWriteBench, a dataset for LLM authorship attribution. It comprises long-form texts (50K+ words per book) generated by frontier LLMs, and is designe…
Predicting Sentence Acceptability Judgments in Multimodal Contexts
Hyewon Jang, Nikolai Ilinykh, Sharid Loáiciga +2
Previous work has examined the capacity of deep neural networks (DNNs), particularly transformers, to predict human sentence acceptability judgments, both independently of context,…
GPSBench: Do Large Language Models Understand GPS Coordinates?
Thinh Hung Truong, Jey Han Lau, Jianzhong Qi
Large Language Models (LLMs) are increasingly deployed in applications that interact with the physical world, such as navigation, robotics, or mapping, making robust geospatial rea…
Context Volume Drives Performance: Tackling Domain Shift in Extremely Low-Resource Translation via RAG
David Samuel Setiawan, Raphaël Merx, Jey Han Lau
Neural Machine Translation (NMT) models for low-resource languages suffer significant performance degradation under domain shift. We quantify this challenge using Dhao, an indigeno…