2 citations · 4 across the 12 of their papers we have counts for
21 papers · 1 filter
EvoEdit: Lifelong Free-Text Knowledge Editing through Latent Perturbation Augmentation and Knowledge-driven Parameter Fusion
Pengfei Cao, Zeao Ji, Daojian Zeng +2
Adjusting the outdated knowledge of large language models (LLMs) after deployment remains a major challenge. This difficulty has spurred the development of knowledge editing, which…
MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation
Xinping Lei, Tong Zhou, Yubo Chen +2
Large Language Models (LLMs) hold substantial potential for accelerating academic ideation but face critical challenges in grounding ideas and mitigating confirmation bias for furt…
Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models
Chenxi Zhou, Pengfei Cao, Jiang Li +4
Post-Training Quantization (PTQ) is a critical strategy for efficient Large Language Models (LLMs) deployment. However, existing scaling laws primarily focus on general performance…
ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework
Ao Chang, Tong Zhou, Yubo Chen +4
Legal Judgment Prediction (LJP) aims to predict judicial outcomes, including relevant legal charge, terms, and fines, which is a crucial process in Large Language Model(LLM). Howev…
Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models
Jiaxiang Liu, Boxuan Xing, Chenhao Yuan +8
As large language models (LLMs) continue to advance, there is a growing urgency to enhance the interpretability of their internal knowledge mechanisms. Consequently, many interpret…
RULE: Reinforcement UnLEarning Achieves Forget-Retain Pareto Optimality
Chenlong Zhang, Zhuoran Jin, Hongbang Yuan +5
The widespread deployment of Large Language Models (LLMs) trained on massive, uncurated corpora has raised growing concerns about the inclusion of sensitive, copyrighted, or illega…