IC-594In LLaMA-2-7B, the truth-correlated attention heads are concentrated after layer 9, with two functional types (structural and associative) evenly distributed throughout the upper portions of the model, showing no further depth-dependent specialisation within that region.

Zheng Yi Ho, Siyuan Liang, Sen Zhang, Yibing Zhan, Dacheng Tao

SourceNoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models

The paper plots the distribution of type-1 (structural) and type-2 (associative) voters across the 32 layers of LLaMA-2-7B and finds that both types are evenly spread out after the ninth layer, with no clear preference for either type at any particular depth. Table 6 on Mistral-7B-Instruct confirms that both voter types exhibit similar individual accuracy (type-1 mean 42.17%, type-2 mean 39.49%) and that no specific localisation of their roles exists within the upper layers. This indicates that the truth-encoding structure is absent in the early layers and present throughout the upper portion, but does not sharpen further with depth.

Evidence

correlational

Key metric

Type-1 voters: 165 heads, mean accuracy 42.17%; Type-2 voters: 86 heads, mean accuracy 39.49% (Mistral-7B-Instruct, TruthfulQA); both types evenly distributed after layer 9 of LLaMA-2-7B

Caveat

The analysis is on two models (LLaMA-2-7B for layer distribution, Mistral-7B-Instruct for accuracy comparison). The paper does not claim the layer-9 boundary is universal across architectures.

Model

Llama 2 / Llama 2 base Llama 2 7B, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1 Mistral-7B-Instruct

Concepts

Depth-dependent structure

Datasets

TruthfulQA / TruthfulQA MC1 [eval]

Related findings

IC-592, IC-593

Extraction

automatic-extraction