"Emergence of Abstractions: Concept Encoding and Decoding Mechanism for In-Context Learning in Transformers"

Transformers learn by mapping concepts to unique mental spaces, create distinct mental neighborhoods for different concepts just like humans organize thoughts.

This paper reveals how transformers learn in-context learning through a concept encoding-decoding mechanism, explaining why they succeed in some tasks but fail in others.

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https://arxiv.org/abs/2412.12276

🤔 Original Problem:

→ While transformers show impressive in-context learning abilities, we don't fully understand how they develop these capabilities or why they perform better on certain tasks[1].

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🔍 Solution in this Paper:

→ The paper introduces a concept encoding-decoding mechanism where transformers map different latent concepts into distinct representation spaces[1].

→ Earlier layers learn to encode concepts while latter layers develop conditional decoding algorithms[1].

→ The model's ability to separate concepts in its representation space directly impacts its performance[1].

→ The researchers validated this mechanism across different model scales using Gemma-2 and Llama-3.1 variants[1].

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💡 Key Insights:

→ Concept encoding and decoding emerge simultaneously during training, suggesting mutual dependence

→ Higher concept decodability correlates with better in-context learning performance

→ Finetuning early layers improves concept encoding more effectively than later layers

→ Models encode commonly seen concepts more clearly than rare ones

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📊 Results:

→ Finetuning first 10 layers outperformed last 10 layers by 37% in POS tagging and 24% in bitwise arithmetic[1]

→ Concept decodability predicted downstream performance across Gemma-2 (2B/9B/27B) and Llama-3.1 (8B/70B)[1]

→ Models achieved near-perfect accuracy on common operators like AND/OR but struggled with XNOR[1]