3 papers
cs.CL2026
RAG or Learning? Understanding the Limits of LLM Adaptation under Continuous Knowledge Drift in the Real World
Hanbing Liu, Lang Cao, Yang Li
Large language models (LLMs) acquire most of their knowledge during pretraining, which ties them to a fixed snapshot of the world and makes adaptation to continuously evolving know…
cs.CL2025
Can LLMs be Good Graph Judge for Knowledge Graph Construction?
Haoyu Huang, Chong Chen, Zeang Sheng +2
In real-world scenarios, most of the data obtained from the information retrieval (IR) system is unstructured. Converting natural language sentences into structured Knowledge Graph…
cs.CL2024
Gradual Learning: Optimizing Fine-Tuning with Partially Mastered Knowledge in Large Language Models
Bozhou Li, Hao Liang, Yang Li +4
During the pretraining phase, large language models (LLMs) acquire vast amounts of knowledge from extensive text corpora. Nevertheless, in later stages such as fine-tuning and infe…