5 citations · 11 across the 10 of their papers we have counts for
13 papers
Memory-Aware Reward Modeling: Learning Human Preferences for LLM Memory Use
Yijiang River Dong, Zexue He, Cheng Yu +9
Memory enables language model assistants to maintain continuity across sessions and generate personalized responses, but knowing more about a user does not necessarily produce resp…
Multi-agent AI systems outperform human teams in creativity
Tiancheng Hu, Yixuan Jiang, Haotian Li +5
Although artificial intelligence (AI) now matches or exceeds human performance across numerous cognitive tasks, creativity remains a highly contested frontier. As AI systems based…
Confident Rankings with Fewer Items: Adaptive LLM Evaluation with Continuous Scores
Esma Balkır, Alice Pernthaller, Marco Basaldella +2
Computerized Adaptive Testing (CAT) has proven effective for efficient LLM evaluation on multiple-choice benchmarks, but modern LLM evaluation increasingly relies on generation tas…
Value of Information: A Framework for Human-Agent Communication
Yijiang River Dong, Tiancheng Hu, Zheng Hui +4
Large Language Model (LLM) agents deployed for real-world tasks face a fundamental dilemma: user requests are underspecified, yet agents must decide whether to act on incomplete in…
Steer Model beyond Assistant: Controlling System Prompt Strength via Contrastive Decoding
Yijiang River Dong, Tiancheng Hu, Zheng Hui +1
Large language models excel at complex instructions yet struggle to deviate from their helpful assistant persona, as post-training instills strong priors that resist conflicting in…
SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
Tiancheng Hu, Joachim Baumann, Lorenzo Lupo +3
Large language model (LLM) simulations of human behavior have the potential to revolutionize the social and behavioral sciences, if and only if they faithfully reflect real human b…
Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging
Tiancheng Hu, Benjamin Minixhofer, Nigel Collier
The "alignment tax" of post-training is typically framed as a drop in task accuracy. We show it also involves a severe loss of calibration, making models overconfident, less reliab…
iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News
Tiancheng Hu, Nigel Collier
Understanding how individuals perceive and react to information is fundamental for advancing social and behavioral sciences and developing human-centered AI systems. Current approa…
When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning
Yijiang River Dong, Tiancheng Hu, Yinhong Liu +2
While Reinforcement Learning from Human Feedback (RLHF) is widely used to align Large Language Models (LLMs) with human preferences, it typically assumes homogeneous preferences ac…
Can LLM be a Personalized Judge?
Yijiang River Dong, Tiancheng Hu, Nigel Collier
Ensuring that large language models (LLMs) reflect diverse user values and preferences is crucial as their user bases expand globally. It is therefore encouraging to see the growin…
LUQ: Long-text Uncertainty Quantification for LLMs
Caiqi Zhang, Fangyu Liu, Marco Basaldella +1
Large Language Models (LLMs) have demonstrated remarkable capability in a variety of NLP tasks. However, LLMs are also prone to generate nonfactual content. Uncertainty Quantificat…
Quantifying the Persona Effect in LLM Simulations
Tiancheng Hu, Nigel Collier
Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. This study investigates how integrating persona variables-demographic, social,…
Generative Language Models Exhibit Social Identity Biases
Tiancheng Hu, Yara Kyrychenko, Steve Rathje +3
The surge in popularity of large language models has given rise to concerns about biases that these models could learn from humans. We investigate whether ingroup solidarity and ou…