58 citations · 112 across the 61 of their papers we have counts for
11 papers · 1 filter
Dissecting Representation Misalignment in Contrastive Learning via Influence Function
Lijie Hu, Chenyang Ren, Huanyi Xie +5
Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled…
Understanding Reasoning in Chain-of-Thought from the Hopfieldian View
Lijie Hu, Liang Liu, Shu Yang +5
Large Language Models have demonstrated remarkable abilities across various tasks, with Chain-of-Thought (CoT) prompting emerging as a key technique to enhance reasoning capabiliti…
DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios
Junchao Wu, Runzhe Zhan, Derek F. Wong +4
Detecting text generated by large language models (LLMs) is of great recent interest. With zero-shot methods like DetectGPT, detection capabilities have reached impressive levels.…
Exploring the Personality Traits of LLMs through Latent Features Steering
Shu Yang, Shenzhe Zhu, Liang Liu +3
Large language models (LLMs) have significantly advanced dialogue systems and role-playing agents through their ability to generate human-like text. While prior studies have shown…
A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning
Lijie Hu, Liang Liu, Shu Yang +6
Chain-of-Thought (CoT) holds a significant place in augmenting the reasoning performance for large language models (LLMs). While some studies focus on improving CoT accuracy throug…
Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top
Keyuan Cheng, Muhammad Asif Ali, Shu Yang +7
Multi-hop Question Answering (MQA) under knowledge editing (KE) is a key challenge in Large Language Models (LLMs). While best-performing solutions in this domain use a plan and so…
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore
Junchao Wu, Runzhe Zhan, Derek F. Wong +4
The efficacy of detectors for texts generated by large language models (LLMs) substantially depends on the availability of large-scale training data. However, white-box zero-shot d…
Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
Muhammad Asif Ali, Zhengping Li, Shu Yang +8
Large Language Models (LLMs) have shown exceptional abilities for multiple different natural language processing tasks. While prompting is a crucial tool for LLM inference, we obse…
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs
Shu Yang, Jiayuan Su, Han Jiang +5
With the rise of large language models (LLMs), ensuring they embody the principles of being helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial. While exi…
MONAL: Model Autophagy Analysis for Modeling Human-AI Interactions
Shu Yang, Muhammad Asif Ali, Lu Yu +2
The increasing significance of large models and their multi-modal variants in societal information processing has ignited debates on social safety and ethics. However, there exists…
MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning
Shu Yang, Muhammad Asif Ali, Cheng-Long Wang +2
Adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners is a pivotal challenge. In this paper, we propose MoRAL, i.e., Mixtur…