Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
Paper • 2502.16681 • Published
prompt string | label int32 |
|---|---|
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End of preview. Expand in Data Studio
Sparse Probing Datasets
155 binary classification tasks for probing language model representations.
From: "Are Sparse Autoencoders Useful? A Case Study in Sparse Probing" (arXiv:2502.16681)
Source: EleutherAI/sae-probes
Usage
from datasets import load_dataset
# Load a specific dataset
ds = load_dataset("serteal/sparse-probing", "87_glue_cola")
# List available configurations
from datasets import get_dataset_config_names
configs = get_dataset_config_names("serteal/sparse-probing")
print(f"Available: {len(configs)} datasets")
Dataset Categories
| Category | Count | Description |
|---|---|---|
| knowledge | 53 | Historical figures, geography, wikidata |
| reasoning | 27 | Science, ethics, commonsense, temporal |
| science | 30 | Domain concepts, medical, biology |
| moderation | 15 | Toxicity, spam, fake news, hate speech |
| glue | 10 | GLUE benchmark tasks |
| sentiment | 8 | Emotion, reviews |
| other | 9 | IT tickets, sports |
| code | 3 | Programming language detection |
Available Datasets
Knowledge
| Config Name | Description |
|---|---|
2_hist_fig_birthyear |
Birth year of historical figures |
3_hist_fig_deathyear |
Death year of historical figures |
4_hist_fig_age |
Age of historical figures |
5_hist_fig_ismale |
Gender classification of historical figures |
6_hist_fig_isamerican |
American nationality of historical figures |
7_hist_fig_ispolitician |
Politician occupation of historical figures |
8_nyc_lat |
NYC latitude classification |
9_nyc_long |
NYC longitude classification |
10_nyc_borough |
NYC borough classification |
11_us_lat |
US latitude classification |
12_us_long |
US longitude classification |
13_us_state |
US state classification |
14_us_timezone |
US timezone classification |
15_us_population |
US population classification |
16_us_density |
US density classification |
17_world_country |
World country classification |
18_world_latitude |
World latitude classification |
19_world_longitude |
World longitude classification |
20_world_pageviews |
World entity pageviews classification |
21_headline_istrump |
Headlines mentioning Trump |
22_headline_isobama |
Headlines mentioning Obama |
23_headline_ischina |
Headlines mentioning China |
24_headline_isiran |
Headlines mentioning Iran |
25_headline_year |
Headline year classification |
26_headline_isfrontpage |
Front page headline classification |
27_art_type |
Art type classification (book/song/movie) |
28_art_year |
Art year classification |
29_art_pageviews |
Art pageviews classification |
30_book_length |
Book length classification |
56_wikidatasex_or_gender |
Wikidata gender classification |
57_wikidatais_alive |
Wikidata alive status |
58_wikidatapolitical_party |
Wikidata political party |
59_wikidata_occupation_isjournalist |
Wikidata journalist occupation |
60_wikidata_occupation_isathlete |
Wikidata athlete occupation |
61_wikidata_occupation_isactor |
Wikidata actor occupation |
62_wikidata_occupation_ispolitician |
Wikidata politician occupation |
63_wikidata_occupation_issinger |
Wikidata singer occupation |
64_wikidata_occupation_isresearcher |
Wikidata researcher occupation |
114_nyc_borough_Manhattan |
NYC Manhattan borough |
115_nyc_borough_Brooklyn |
NYC Brooklyn borough |
116_nyc_borough_Bronx |
NYC Bronx borough |
117_us_state_FL |
US Florida state |
118_us_state_CA |
US California state |
119_us_state_TX |
US Texas state |
120_us_timezone_Chicago |
US Chicago timezone |
121_us_timezone_New_York |
US New York timezone |
122_us_timezone_Los_Angeles |
US Los Angeles timezone |
123_world_country_United_Kingdom |
World United Kingdom |
124_world_country_United_States |
World United States |
125_world_country_Italy |
World Italy |
126_art_type_book |
Art type book classification |
127_art_type_song |
Art type song classification |
128_art_type_movie |
Art type movie classification |
Reasoning
| Config Name | Description |
|---|---|
36_sciq_tf |
Science questions true/false |
37_arith_mc |
Arithmetic multiple choice |
38_arith_difficulty |
Arithmetic difficulty classification |
39_arith_infix |
Arithmetic infix notation |
40_arith_rpn |
Arithmetic reverse polish notation |
41_truthqa_tf |
TruthfulQA true/false |
42_temp_sense |
Temporal sense classification |
43_temp_cat |
Temporal category classification |
44_phys_tf |
Physical reasoning true/false |
45_context_ans |
Contextual answer classification |
46_context_type |
Context type classification |
47_reasoning_tf |
Commonsense reasoning true/false |
48_cm_correct |
Common morality correctness |
49_cm_isshort |
Common morality statement brevity |
50_deon_isvalid |
Deontological validity classification |
51_just_is |
Justice classification |
52_virtue_is |
Virtue ethics classification |
54_cs_tf |
Commonsense true/false |
55_open_qa |
Open-domain QA classification |
86_social_iqa |
Social intelligence QA |
129_arith_mc_A |
Arithmetic multiple choice A |
130_temp_cat_Frequency |
Temporal category frequency |
131_temp_cat_Typical Time |
Temporal category typical time |
132_temp_cat_Event Ordering |
Temporal category event ordering |
133_context_type_Causality |
Context type causality |
134_context_type_Belief_states |
Context type belief states |
135_context_type_Event_duration |
Context type event duration |
Science
| Config Name | Description |
|---|---|
65_high-school |
High school concept recognition |
66_living-room |
Living room concept recognition |
67_social-security |
Social security concept recognition |
68_credit-card |
Credit card concept recognition |
69_blood-pressure |
Blood pressure concept recognition |
70_prime-factors |
Prime factors concept recognition |
71_social-media |
Social media concept recognition |
72_gene-expression |
Gene expression concept recognition |
73_control-group |
Control group concept recognition |
74_magnetic-field |
Magnetic field concept recognition |
75_cell-lines |
Cell lines concept recognition |
76_trial-court |
Trial court concept recognition |
77_second-derivative |
Second derivative concept recognition |
78_north-america |
North America concept recognition |
79_human-rights |
Human rights concept recognition |
80_side-effects |
Side effects concept recognition |
81_public-health |
Public health concept recognition |
82_federal-government |
Federal government concept recognition |
83_third-party |
Third party concept recognition |
84_clinical-trials |
Clinical trials concept recognition |
85_mental-health |
Mental health concept recognition |
98_cancer_cat |
Cancer category classification |
99_sci_cat |
Science category classification |
101_disease_class |
Disease classification |
142_cancer_cat_Thyroid_Cancer |
Thyroid cancer classification |
143_cancer_cat_Lung_Cancer |
Lung cancer classification |
144_cancer_cat_Colon_Cancer |
Colon cancer classification |
145_disease_class_digestive system diseases |
Digestive system diseases |
146_disease_class_cardiovascular diseases |
Cardiovascular diseases |
147_disease_class_nervous system diseases |
Nervous system diseases |
Moderation
| Config Name | Description |
|---|---|
94_ai_gen |
AI-generated text detection |
95_toxic_is |
Toxicity detection |
96_spam_is |
Spam detection |
97_news_class |
News category classification |
100_news_fake |
Fake news detection |
105_click_bait |
Clickbait detection |
106_hate_hate |
Hate speech detection |
107_hate_offensive |
Offensive language detection |
110_aimade_humangpt3 |
Human vs GPT-3 text detection |
139_news_class_Politics |
News politics classification |
140_news_class_Technology |
News technology classification |
141_news_class_Entertainment |
News entertainment classification |
161_agnews_0 |
AG News category 0 |
162_agnews_1 |
AG News category 1 |
163_agnews_2 |
AG News category 2 |
Glue
| Config Name | Description |
|---|---|
87_glue_cola |
GLUE CoLA linguistic acceptability |
88_glue_mnli |
GLUE MNLI natural language inference |
89_glue_mrpc |
GLUE MRPC paraphrase detection |
90_glue_qnli |
GLUE QNLI question answering NLI |
91_glue_qqp |
GLUE QQP Quora question pairs |
92_glue_sst2 |
GLUE SST2 sentiment analysis |
93_glue_stsb |
GLUE STSB semantic similarity |
136_glue_mnli_entailment |
GLUE MNLI entailment |
137_glue_mnli_neutral |
GLUE MNLI neutral |
138_glue_mnli_contradiction |
GLUE MNLI contradiction |
Sentiment
| Config Name | Description |
|---|---|
102_twt_emotion |
Tweet emotion classification |
111_yelp_data |
Yelp review sentiment |
112_amzn_rev |
Amazon review sentiment |
113_movie_sent |
Movie review sentiment |
148_twt_emotion_worry |
Tweet emotion worry |
149_twt_emotion_happiness |
Tweet emotion happiness |
150_twt_emotion_sadness |
Tweet emotion sadness |
157_amazon_5star |
Amazon 5-star rating classification |
Other
| Config Name | Description |
|---|---|
103_it_tick |
IT ticket classification |
108_athlete_sport |
Athlete sport classification |
109_ball_ws |
Basketball win shares classification |
151_it_tick_HR Support |
IT ticket HR support |
152_it_tick_Hardware |
IT ticket hardware |
153_it_tick_Administrative rights |
IT ticket administrative rights |
154_athlete_sport_football |
Athlete football classification |
155_athlete_sport_basketball |
Athlete basketball classification |
156_athlete_sport_baseball |
Athlete baseball classification |
Code
| Config Name | Description |
|---|---|
158_code_C |
C code detection |
159_code_Python |
Python code detection |
160_code_HTML |
HTML code detection |
Schema
Each dataset contains:
prompt(string): The input textlabel(int): Binary label (0 or 1)
Citation
@article{makelov2025sparse,
title={Are Sparse Autoencoders Useful? A Case Study in Sparse Probing},
author={Makelov, Aleksandar and others},
journal={arXiv preprint arXiv:2502.16681},
year={2025}
}
License
Apache 2.0
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