| ---
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| license: cc-by-4.0
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| language:
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| - en
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| - de
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| - fr
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| - zh
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| tags:
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| - interpretability
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| - llm-channels
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| - wordnet
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| pretty_name: LCN Paper Data (TPAMI channels paper)
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| ---
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|
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| # LCN Paper Data
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| Key data backing the figures and lexical analysis of the TPAMI channels paper.
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|
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| ## dictionaries/ (词典 / 大全)
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| Token -> WordNet sense dictionaries.
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| - super_thesaurus.json (2.1 MB): flat dict {word: "<wordnet.lexname>"} (词典).
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| - expanded_thesaurus.json (6.5 MB): {word: ["sense.n.01", ...]} full senses (大全).
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|
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| ## bridge_coords/
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| 2-D PCA coords for the fig:bridge panels (pooled + per-token).
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| - phase3a_*: cross-lingual EN vs DE/FR/ZH (Llama3-1B, Llama3-8B, Qwen3-1.7B, Qwen3-8B).
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| - phase3b_*: cross-modal text vs vision-language (Qwen2.5-VL-7B, Qwen3-VL-2B, Qwen3-VL-8B, InternVL3-8B).
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| The *_pertoken.json files carry a top_k list so an alternate token can be picked per panel with no GPU rerun.
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|
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| ## FIGURE_SOURCE_DATA_MAP.md
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| Catalog: each figure -> raw data file -> server -> generation script. GB-scale raw *_direction_space_raw_*.json live on the Leonardo cluster and are not mirrored here.
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| ## token_channel_dicts/ (token <-> feature/channel-dimension 大词典)
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| Maps each shared-concept token to the model feature dimensions (channels) it
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| activates. This is the token<->channel mapping behind the figures.
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|
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| ### multilingual_llm/fig10_ml_shared_channels.json (2.9 MB)
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| { "shared_channels": { "<token>": { "shared_channels": ["individual_18_K_18_ch937", ...], "n_shared": int, "overlap_ratio": float, "pair_overlaps": ..., "per_lang_top": ... } }, "pca": {...} }
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| Channel ID format encodes the feature/channel index C.
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|
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| ### multimodal_vlm/ (4 VLMs: internvl3_2b, internvl3_8b, qwen3vl_2b, llava_ov_7b)
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| - mm_shared_token_channels_<model>.json: { items: [ { token, channels: [ {layer, channel, max_activation}, ... ] } ] } -- channels where the token is top-10 in BOTH vision-language and text.
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| - mm_bridge_per_token_channels_<model>.json: cross-modal bridge, per-token top-k channels per layer (layers 15/20/25).
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| Here is the literal feature-dimension index within .
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| ## token_channel_dicts/ (token <-> feature/channel-dimension big dictionary)
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| Maps each shared-concept token to the model feature dimensions (channels) it
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| activates. This is the token<->channel mapping behind the figures.
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|
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| ### multilingual_llm/fig10_ml_shared_channels.json (2.9 MB)
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| Schema:
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| `{"shared_channels": {"<token>": {"shared_channels": ["individual_18_K_18_ch937", ...], "n_shared": int, "overlap_ratio": float, "pair_overlaps": ..., "per_lang_top": ...}}, "pca": {...}}`
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| The channel ID string `individual_<i>_K_<k>_ch<C>` encodes the feature/channel index C.
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|
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| ### multimodal_vlm/ (4 VLMs: internvl3_2b, internvl3_8b, qwen3vl_2b, llava_ov_7b)
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| - `mm_shared_token_channels_<model>.json`:
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| `{"items": [{"token": str, "channels": [{"layer": str, "channel": int, "max_activation": float}, ...]}]}`
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| channels where the token is top-10 in BOTH vision-language and text.
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| - `mm_bridge_per_token_channels_<model>.json`: cross-modal bridge, per-token top-k channels per layer (layers 15/20/25).
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| Here `channel` is the literal feature-dimension index within `layer`.
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| ## thesaurus/ (channel self-aggregation: grouping + selectivity)
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| Training-free analysis of whether a channel's top-activating tokens cluster into near-synonyms /
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| same-category words (indicator 1), and whether a channel monopolizes specific dataset tokens
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| (indicator 3). 6 models: Llama-3.2-1B, Llama-3.1-8B, Gemma-3-1B, Gemma-3-4B, Qwen3-1.7B, Qwen3-8B
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| (self-gen on tatsu-lab/alpaca). Uses the dictionaries/ files above.
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|
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| ### Top-level tables (self-contained)
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| - `MASTER_TABLE.md` - master table: 6 models x K in {3,5,10,20,30,50} x 5 grouping metrics x 3 methods
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| (WordNet-synonym / WordNet-category / MiniLM cos>=0.4/0.45/0.5) x 2 scopes, real vs random, + indicator 3.
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| - `SELECTIVITY_SWEEP_TABLE.md` - selectivity@K real vs random, 6 models.
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| - `SELECTIVITY_SCALE_COMPARE.md` - 400 -> 5000/20000 data-scaling de-bias.
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| - `SELECTIVITY_ACT_TABLE.md` - top-1-by-activation selectivity (noise-diagnosed).
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|
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| ### Indicator 3 selectivity
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| selectivity@K(channel) = mean of min(num/den,1) over the channel's top-K activation tokens.
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| num = how often the channel high-activates that token (na-count); den = that token's total
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| occurrences in all N self-gen answers (tokenizer, strip-aligned). Random = redistribution null
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| (redistribute each token's high-activation events uniformly across the module's channels) -> ~0 at scale.
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| Finding: scaling data removes small-sample inflation, but real stays >> random (~10x at 20k).
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| qwen3_1b has full 20000 (`data/sel_sweep_qwen3_1b_n20000.json`); others at 400 + partial scale.
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|
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| ### data/ and code/
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| - `data/sel_sweep_<model>[_na400|_n5000|_n20000].json` - selectivity@K per sample size.
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| - `data/random_v2_*` (vocab/alpaca null), `data/random_perm_*` (permutation null), `data/results_*`, `data/sel_act_*`.
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| - `code/` - grouping core (thesaurus_grouping.py), selectivity_sweep/act, group_metric_sweep,
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| gen_master_table, random_baseline_v2, split/merge_chunks (20k chunk->merge pipeline), build_super_thesaurus.
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| |
