concept
active
concept:meta-llama-3-1-8b-instructMeta-Llama-3.1-8B-Instruct
Backbone model used in E3 geometry analysis.
Neighborhood — ranked by edge-count
Papers (1)
paper
Methods (1)
method
- Quantitative study correlating layer-wise anchoring geometry (S_max, AUS_N) with behavioral thresholds θ50
Concepts (5)
concept
- Llama-3.1-8B-Instructrelated_toPrimary qualitative demonstration model and one of 14 LLMs benchmarked
- Llama-3.3-70B-Instructrelated_toPrimary model of interest showing substantial ESR; largest model tested in the study
- Llama-3.2-3B-Instructrelated_to3B Llama model tested; used for injection stride visualization
- LLaMA3.1-8Brelated_toOne of four LLMs selected for representation analysis; embedding dimension D=4096; used as demonstration model in scatter plots.
- Llama-3.2-1B-Instructrelated_toSmallest Llama model tested; benchmarked across all injection methods
Findings (2)
finding
- Meta-LLaMA-3.1-8B-Instruct shows optimal anchoring at layer 9 (S ≈ −1.90, median peak layer ℓ* = 10 [IQR 0.384])associated_withE3 result establishing the Goldilocks zone at mid-layers for LLaMA architecture
- LLaMA-3.1-8B: Sbmax = -1.896 ± 0.211, AUSN = -2.119 ± 0.198, peak layer ℓ* = 10 (median)associated_withSeed-pooled geometry-only statistics (per-dev z units).
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- 32B OLMo model quantized to 4-bit NF4; tested in OCEAN benchmarks
- One of four LLMs selected; larger model with D=8192 embedding dimension; analyzed across proportionally aligned layers.
- Large open-weight model showing compliance gap in helpful-only setting
- The model used in Experiment 2 for SAE feature steering experiments via Goodfire API
- 7B OLMo model tested; used for layerwise steering visualization (Figure 4)
- Demonstrates ESR can be deliberately enhanced through prompting in the largest model
- Model-specific difference in persona susceptibility
- Language model family used in cross-modal alignment experiments across multiple sizes