5 papers · 1 filter
TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation
Xinliang Frederick Zhang, Lu Wang
Personalized large language models (PLLMs) have garnered significant attention for their ability to align outputs with individual's needs and preferences. However, they still strug…
Do LLMs Really Need 10+ Thoughts for "Find the Time 1000 Days Later"? Towards Structural Understanding of LLM Overthinking
Xinliang Frederick Zhang, Anhad Mohananey, Alexandra Chronopoulou +5
Models employing long chain-of-thought (CoT) reasoning have shown superior performance on complex reasoning tasks. Yet, this capability introduces a critical and often overlooked i…
PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang
Large language model (LLM) personalization aims to align model outputs with individuals' unique preferences and opinions. While recent efforts have implemented various personalizat…
Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives
Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang
Reasoning about time and temporal relations is an integral aspect of human cognition, essential for perceiving the world and navigating our experiences. Though large language model…
MOKA: Moral Knowledge Augmentation for Moral Event Extraction
Xinliang Frederick Zhang, Winston Wu, Nick Beauchamp +1
News media often strive to minimize explicit moral language in news articles, yet most articles are dense with moral values as expressed through the reported events themselves. How…