LLMs' concept learning dynamics revealed through circuit analysis

A new research paper explores how large language models (LLMs) acquire, retain, and forget concepts during continual pre-training. The study links these dynamics to the models' internal "concept circuits" and uses graph metrics to analyze circuit topology. Findings indicate that LLMs' concept circuits offer a consistent signal of learning and forgetting, with concept circuits showing a stage-wise temporal pattern. The research also suggests that concepts with higher learning gains may experience greater forgetting, and semantically similar concepts interfere more than weakly related ones. AI

IMPACT Provides insights into LLM learning mechanisms, potentially guiding future training strategies for better concept retention.

RANK_REASON Research paper published on arXiv detailing findings about LLM concept learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• alphaXiv
• arXiv
• Barry Menglong Yao
• Circuit-aware Experience Replay
• concept
• Continual Pre-Training
• Hugging Face
• large-language models

• paper
• model release

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