Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
models: list<item: string>
child 0, item: string
coords: struct<Llama3-1B: struct<token: string, en: list<item: list<item: double>>, ot: list<item: list<item (... 1574 chars omitted)
child 0, Llama3-1B: struct<token: string, en: list<item: list<item: double>>, ot: list<item: list<item: double>>, en_cen (... 304 chars omitted)
child 0, token: string
child 1, en: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 2, ot: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 3, en_centroid: list<item: double>
child 0, item: double
child 4, ot_centroid: list<item: double>
child 0, item: double
child 5, sep: double
child 6, ntok: int64
child 7, top_k: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64, en: list<item: list<item: do (... 110 chars omitted)
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64, en: list<item: list<item: double>>, ot: (... 98 chars omitted)
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
child 4, en: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 5, ot: list<item: list<item: double>>
...
ouble
child 7, ot_centroid: list<item: double>
child 0, item: double
top_k: struct<Llama3-1B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>, Llama3-8 (... 246 chars omitted)
child 0, Llama3-1B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64>
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
child 1, Llama3-8B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64>
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
child 2, Qwen3-1.7B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64>
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
child 3, Qwen3-8B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64>
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
other_langs: list<item: string>
child 0, item: string
to
{'models': List(Value('string')), 'other_langs': List(Value('string')), 'coords': {'Llama3-1B': {'en': List(List(Value('float64'))), 'ot': List(List(Value('float64'))), 'en_centroid': List(Value('float64')), 'ot_centroid': List(Value('float64')), 'sep': Value('float64'), 'ntok': Value('int64')}, 'Llama3-8B': {'en': List(List(Value('float64'))), 'ot': List(List(Value('float64'))), 'en_centroid': List(Value('float64')), 'ot_centroid': List(Value('float64')), 'sep': Value('float64'), 'ntok': Value('int64')}, 'Qwen3-1.7B': {'en': List(List(Value('float64'))), 'ot': List(List(Value('float64'))), 'en_centroid': List(Value('float64')), 'ot_centroid': List(Value('float64')), 'sep': Value('float64'), 'ntok': Value('int64')}, 'Qwen3-8B': {'en': List(List(Value('float64'))), 'ot': List(List(Value('float64'))), 'en_centroid': List(Value('float64')), 'ot_centroid': List(Value('float64')), 'sep': Value('float64'), 'ntok': Value('int64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
models: list<item: string>
child 0, item: string
coords: struct<Llama3-1B: struct<token: string, en: list<item: list<item: double>>, ot: list<item: list<item (... 1574 chars omitted)
child 0, Llama3-1B: struct<token: string, en: list<item: list<item: double>>, ot: list<item: list<item: double>>, en_cen (... 304 chars omitted)
child 0, token: string
child 1, en: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 2, ot: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 3, en_centroid: list<item: double>
child 0, item: double
child 4, ot_centroid: list<item: double>
child 0, item: double
child 5, sep: double
child 6, ntok: int64
child 7, top_k: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64, en: list<item: list<item: do (... 110 chars omitted)
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64, en: list<item: list<item: double>>, ot: (... 98 chars omitted)
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
child 4, en: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 5, ot: list<item: list<item: double>>
...
ouble
child 7, ot_centroid: list<item: double>
child 0, item: double
top_k: struct<Llama3-1B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>, Llama3-8 (... 246 chars omitted)
child 0, Llama3-1B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64>
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
child 1, Llama3-8B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64>
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
child 2, Qwen3-1.7B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64>
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
child 3, Qwen3-8B: list<item: struct<token: string, sep: double, n_en: int64, n_ot: int64>>
child 0, item: struct<token: string, sep: double, n_en: int64, n_ot: int64>
child 0, token: string
child 1, sep: double
child 2, n_en: int64
child 3, n_ot: int64
other_langs: list<item: string>
child 0, item: string
to
{'models': List(Value('string')), 'other_langs': List(Value('string')), 'coords': {'Llama3-1B': {'en': List(List(Value('float64'))), 'ot': List(List(Value('float64'))), 'en_centroid': List(Value('float64')), 'ot_centroid': List(Value('float64')), 'sep': Value('float64'), 'ntok': Value('int64')}, 'Llama3-8B': {'en': List(List(Value('float64'))), 'ot': List(List(Value('float64'))), 'en_centroid': List(Value('float64')), 'ot_centroid': List(Value('float64')), 'sep': Value('float64'), 'ntok': Value('int64')}, 'Qwen3-1.7B': {'en': List(List(Value('float64'))), 'ot': List(List(Value('float64'))), 'en_centroid': List(Value('float64')), 'ot_centroid': List(Value('float64')), 'sep': Value('float64'), 'ntok': Value('int64')}, 'Qwen3-8B': {'en': List(List(Value('float64'))), 'ot': List(List(Value('float64'))), 'en_centroid': List(Value('float64')), 'ot_centroid': List(Value('float64')), 'sep': Value('float64'), 'ntok': Value('int64')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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": { "": { "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_.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_.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). Herechannelis the literal feature-dimension index withinlayer.
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.
- Downloads last month
- 26