---
license: cc-by-4.0
language:
- en
- de
- fr
- zh
tags:
- interpretability
- llm-channels
- wordnet
pretty_name: LCN Paper Data (TPAMI channels paper)
---

# LCN Paper Data

Key data backing the figures and lexical analysis of the TPAMI channels paper.

## dictionaries/ (词典 / 大全)
Token -> WordNet sense dictionaries.
- super_thesaurus.json (2.1 MB): flat dict {word: "<wordnet.lexname>"} (词典).
- expanded_thesaurus.json (6.5 MB): {word: ["sense.n.01", ...]} full senses (大全).

## bridge_coords/
2-D PCA coords for the fig:bridge panels (pooled + per-token).
- phase3a_*: cross-lingual EN vs DE/FR/ZH (Llama3-1B, Llama3-8B, Qwen3-1.7B, Qwen3-8B).
- phase3b_*: cross-modal text vs vision-language (Qwen2.5-VL-7B, Qwen3-VL-2B, Qwen3-VL-8B, InternVL3-8B).
The *_pertoken.json files carry a top_k list so an alternate token can be picked per panel with no GPU rerun.

## FIGURE_SOURCE_DATA_MAP.md
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.


## token_channel_dicts/ (token <-> feature/channel-dimension 大词典)
Maps each shared-concept token to the model feature dimensions (channels) it
activates. This is the token<->channel mapping behind the figures.

### multilingual_llm/fig10_ml_shared_channels.json (2.9 MB)
{ "shared_channels": { "<token>": { "shared_channels": ["individual_18_K_18_ch937", ...], "n_shared": int, "overlap_ratio": float, "pair_overlaps": ..., "per_lang_top": ... } }, "pca": {...} }
Channel ID format  encodes the feature/channel index C.

### multimodal_vlm/ (4 VLMs: internvl3_2b, internvl3_8b, qwen3vl_2b, llava_ov_7b)
- 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.
- mm_bridge_per_token_channels_<model>.json: cross-modal bridge, per-token top-k channels per layer (layers 15/20/25).
Here  is the literal feature-dimension index within .


## token_channel_dicts/ (token <-> feature/channel-dimension big dictionary)
Maps each shared-concept token to the model feature dimensions (channels) it
activates. This is the token<->channel mapping behind the figures.

### multilingual_llm/fig10_ml_shared_channels.json (2.9 MB)
Schema:
`{"shared_channels": {"<token>": {"shared_channels": ["individual_18_K_18_ch937", ...], "n_shared": int, "overlap_ratio": float, "pair_overlaps": ..., "per_lang_top": ...}}, "pca": {...}}`
The channel ID string `individual_<i>_K_<k>_ch<C>` encodes the feature/channel index C.

### multimodal_vlm/ (4 VLMs: internvl3_2b, internvl3_8b, qwen3vl_2b, llava_ov_7b)
- `mm_shared_token_channels_<model>.json`:
  `{"items": [{"token": str, "channels": [{"layer": str, "channel": int, "max_activation": float}, ...]}]}`
  channels where the token is top-10 in BOTH vision-language and text.
- `mm_bridge_per_token_channels_<model>.json`: cross-modal bridge, per-token top-k channels per layer (layers 15/20/25).
Here `channel` is the literal feature-dimension index within `layer`.


## thesaurus/ (channel self-aggregation: grouping + selectivity)

Training-free analysis of whether a channel's top-activating tokens cluster into near-synonyms /
same-category words (indicator 1), and whether a channel monopolizes specific dataset tokens
(indicator 3). 6 models: Llama-3.2-1B, Llama-3.1-8B, Gemma-3-1B, Gemma-3-4B, Qwen3-1.7B, Qwen3-8B
(self-gen on tatsu-lab/alpaca). Uses the dictionaries/ files above.

### Top-level tables (self-contained)
- `MASTER_TABLE.md` - master table: 6 models x K in {3,5,10,20,30,50} x 5 grouping metrics x 3 methods
  (WordNet-synonym / WordNet-category / MiniLM cos>=0.4/0.45/0.5) x 2 scopes, real vs random, + indicator 3.
- `SELECTIVITY_SWEEP_TABLE.md` - selectivity@K real vs random, 6 models.
- `SELECTIVITY_SCALE_COMPARE.md` - 400 -> 5000/20000 data-scaling de-bias.
- `SELECTIVITY_ACT_TABLE.md` - top-1-by-activation selectivity (noise-diagnosed).

### Indicator 3 selectivity
selectivity@K(channel) = mean of min(num/den,1) over the channel's top-K activation tokens.
num = how often the channel high-activates that token (na-count); den = that token's total
occurrences in all N self-gen answers (tokenizer, strip-aligned). Random = redistribution null
(redistribute each token's high-activation events uniformly across the module's channels) -> ~0 at scale.
Finding: scaling data removes small-sample inflation, but real stays >> random (~10x at 20k).
qwen3_1b has full 20000 (`data/sel_sweep_qwen3_1b_n20000.json`); others at 400 + partial scale.

### data/  and  code/
- `data/sel_sweep_<model>[_na400|_n5000|_n20000].json` - selectivity@K per sample size.
- `data/random_v2_*` (vocab/alpaca null), `data/random_perm_*` (permutation null), `data/results_*`, `data/sel_act_*`.
- `code/` - grouping core (thesaurus_grouping.py), selectivity_sweep/act, group_metric_sweep,
  gen_master_table, random_baseline_v2, split/merge_chunks (20k chunk->merge pipeline), build_super_thesaurus.