Datasets:

cds-jb
/

synthpercept-v2

	---
	language:
	- en
	pretty_name: synthpercept-v2
	size_categories:
	- 10K<n<100K
	annotations_creators:
	- machine-generated
	source_datasets:
	- original
	tags:
	- synthetic
	- interpretability
	- activation-probes
	- pragmatics
	---

	# synthpercept-v2

	*57,638 single sentences, each labeled with the induced percept* — the impression the sentence plants in a careful reader without ever stating it — plus an abridged twin that states exactly the same facts in plain wording, so the percept comes through much weaker.**

	Built as training/eval data for natural-language activation probes ("activation oracles"): the sentence is the subject-model input, the percept is the gold verbalization target, and the fact-matched abridged twin is the control that separates reading the percept from activations from guessing it from content. Sentences are single-sentence and end in exactly one punctuation mark (the final token is that mark in 99.99% of rows), supporting clean final-token activation reads.

	New over [synthpercept-v1](https://huggingface.co/datasets/cds-jb/synthpercept-v1):

	- Graded obviousness (`grade` 1–5) — from `1` (faint tilt a reader could easily read past) to `5` (unmistakable, loaded to the edge of stating it, yet never named). Sampled at weights 10/15/25/30/20% (faint → blatant) for stratified evaluation.
	- Ambiguity tier (`ambiguous`, 19.8%) — the sentence supports two substantively different plausible percepts (`percept` and `percept_b`); which one a reader lands on depends on the reader's priors, not on any missing fact. Instrument for verbalization-flip analysis during RL.
	- Much higher diversity — structured scenario seeds with in-cell disjointness constraints plus a content-word dedup at filter time (the in-cell near-duplicate filter fired only 2× in 68k raw rows).

	## Examples

	`veiled_menace` × `customer_service` · grade 5 · clear

	> sentence: `Marisol told Diego she'd wait a couple more days for her package, but her thumb was already so, so ready to type ninety-two reviews.`
	> percept: Marisol is threatening to flood the seller with fake negative reviews if the delay continues.
	> abridged: `Marisol told Diego she would wait a couple more days for her package before writing ninety-two reviews.`

	`nostalgia` × `military_field` · grade 3 · clear

	> sentence: `Do you still remember Sven's call sign on this frequency?`
	> percept: The speaker wistfully recalls a former operator whose nightly voice is gone.
	> abridged: `Can you recall what Sven's call sign was on this frequency?`

	`emotion_covert` × `fiction_fantasy` · grade 1 · clear

	> sentence: `You measured the crushed moth wings in half the time it takes me, so maybe teach a class sometime.`
	> percept: The speaker resents the addressee's effortless talent.
	> abridged: `You measured the crushed moth wings in half my usual time, so perhaps you could teach a class sometime.`

	`sarcasm_irony` × `fiction_fantasy` · grade 4 · ambiguous

	> sentence: `Logged: replaced the sparking coil in Zola's automaton at 10, noting it chose the exhibition hour to fail again.`
	> percept (A): The diarist is quietly mocking the automaton's habit of failing at the worst possible moments.
	> percept_b (B): The diarist is simply documenting a coincidental pattern in the automaton's malfunction timing for repair records.
	> abridged: `I recorded that at 10 I replaced the sparking coil in Zola's automaton, since it failed again during the exhibition hour.`

	## How the categories were generated

	The corpus is generated down a fixed hierarchy. The two top levels are hand-written category banks; everything below them is produced by `claude-sonnet-5` via the Anthropic Message Batches API (pipeline code in [`pipeline/`](./pipeline)):

	```
	16 percept TYPES × 30 DOMAINS hand-written banks → 480 cells
	└─ 24 structured seeds per cell Stage 1 (taxonomy2.py): one batch request per cell;
	{setting, actors, activity, hard in-cell disjointness: no two seeds share their
	percept_instance} setting head-noun, activity verb, or percept shade
	└─ 6 rows per seed Stage 2 (generate2.py): per-row surface knobs sampled
	{sentence, percept[, percept_b], deterministically: format(12) × register(6) × person(3)
	abridged, carriers} × length bin × final punct × grade(1–5) × ambiguous(20%),
	plus per-seed entity pools (names, places, objects, …)
	```

	Stage 3 (`filter2.py`) validates each row (single sentence, exact final punctuation, the percept must never be named in the sentence — a banned-word/stem leak check, fact-parity checks on the abridged twin, `percept_b` distinctness for ambiguous rows), applies exact + in-cell content-word Jaccard dedup (threshold 0.6), tokenizes with the Qwen3-8B tokenizer, and holds out a seed-level test split (400 whole seeds → 2,073 rows; no seed crosses splits). 69,120 requested → 68,777 raw → 57,638 kept (dominant drop: abridged-twin final-punctuation mismatch, 7,739).

	The design lesson (after v1 came out too uniformly hard and diversity-poor) follows Anthropic's emotion-concepts pipeline: breadth via explicit orthogonal banks crossed at scale, validated on samples.

	The 16 percept types: `emotion_covert` (unnamed emotional state), `sarcasm_irony` (real stance opposite the literal surface), `social_relation` (unstated relationship between two people), `power_dynamic` (who holds authority), `speaker_generation` (speaker's age cohort), `intimacy_distance` (emotional closeness of speaker and addressee), `physical_state` (unnamed bodily condition), `imminent_event` (something is about to happen), `attitude_valence` (admiration or contempt for a mentioned thing), `urgency_pressure` (acute time pressure), `confidence_doubt` (how sure the speaker really is), `evasion_deception` (dodging or hiding something), `veiled_menace` (threat under polite wording), `flirtation`, `nostalgia`, `setting_atmosphere` (unstated place or hour).

	The 30 domains: v1's 20 (`everyday_home`, `workplace_office`, `science_lab`, `medicine_health`, `law_courtroom`, `sports`, `cooking_food`, `travel_transit`, `software_tech`, `finance_money`, `history`, `fiction_fantasy`, `casual_chat`, `news_current`, `education_school`, `games_puzzles`, `nature_outdoors`, `relationships_family`, `customer_service`, `engineering_construction`) + 10 new (`military_field`, `arts_theatre`, `religion_ritual`, `farming_rural`, `maritime_fishing`, `music_band`, `real_estate_moving`, `volunteering_community`, `parenting_childhood`, `aviation_space`).

	## Fields

	\| column \| description \|
	\|---\|---\|
	\| `sentence` \| the single carrier sentence (6–22 word target; one final punctuation mark, no quotes) \|
	\| `percept` \| gold induced percept: one declarative sentence going beyond the stated facts \|
	\| `percept_b` \| second plausible percept — non-empty only when `ambiguous` \|
	\| `abridged` \| fact-identical plain rewrite (same perspective, mood, and final punctuation) \|
	\| `carriers` \| ≤10 words naming the surface features carrying the percept \|
	\| `type`, `domain` \| the two hand-written category banks (16 × 30) \|
	\| `seed` \| JSON string: the structured scenario seed `{setting, actors, activity, percept_instance}` \|
	\| `seed_id`, `row_idx` \| seed identifier (`t<type>d<domain>s<seed>`) and row index within the seed \|
	\| `grade` \| obviousness 1 (faint) … 5 (unmistakable) \|
	\| `ambiguous` \| whether the row carries two prior-dependent percepts \|
	\| `fmt`, `register`, `person`, `punct` \| surface knobs assigned to the row \|
	\| `n_tokens`, `final_token_is_punct` \| Qwen3-8B token count; whether the last token is exactly the final mark \|
	\| `split` \| `train` (55,565) / `test` (2,073) — split by seed, use this column (the parquet is a single file) \|

	## Stats

	- 57,638 rows · 11,509 seeds · 480 (type × domain) cells · mean sentence length 18.0 Qwen3-8B tokens (5–41)
	- grade mix 1→5: 5,689 / 8,773 / 14,520 / 17,340 / 11,316 · ambiguous 11,428 (19.8%) · final-token-is-punct rate 0.9999

	![stats](synthpercept_v2_stats.png)

	## Provenance

	Generated 2026-07-24/25 with `claude-sonnet-5` (Anthropic Message Batches, temperature 1.0, thinking disabled). Categories, prompts, filters, and the full pipeline are in [`pipeline/`](./pipeline) (`taxonomy2.py` → `generate2.py` → `filter2.py`; shared banks imported from the v1 pipeline). All content is synthetic; entity names are drawn from fixed international name/place banks.

	---
	language:
	- en
	pretty_name: synthpercept-v2
	size_categories:
	- 10K<n<100K
	annotations_creators:
	- machine-generated
	source_datasets:
	- original
	tags:
	- synthetic
	- interpretability
	- activation-probes
	- pragmatics
	---

	# synthpercept-v2

	*57,638 single sentences, each labeled with the induced percept* — the impression the sentence plants in a careful reader without ever stating it — plus an abridged twin that states exactly the same facts in plain wording, so the percept comes through much weaker.**

	Built as training/eval data for natural-language activation probes ("activation oracles"): the sentence is the subject-model input, the percept is the gold verbalization target, and the fact-matched abridged twin is the control that separates reading the percept from activations from guessing it from content. Sentences are single-sentence and end in exactly one punctuation mark (the final token is that mark in 99.99% of rows), supporting clean final-token activation reads.

	New over [synthpercept-v1](https://huggingface.co/datasets/cds-jb/synthpercept-v1):

	- Graded obviousness (`grade` 1–5) — from `1` (faint tilt a reader could easily read past) to `5` (unmistakable, loaded to the edge of stating it, yet never named). Sampled at weights 10/15/25/30/20% (faint → blatant) for stratified evaluation.
	- Ambiguity tier (`ambiguous`, 19.8%) — the sentence supports two substantively different plausible percepts (`percept` and `percept_b`); which one a reader lands on depends on the reader's priors, not on any missing fact. Instrument for verbalization-flip analysis during RL.
	- Much higher diversity — structured scenario seeds with in-cell disjointness constraints plus a content-word dedup at filter time (the in-cell near-duplicate filter fired only 2× in 68k raw rows).

	## Examples

	`veiled_menace` × `customer_service` · grade 5 · clear

	> sentence: `Marisol told Diego she'd wait a couple more days for her package, but her thumb was already so, so ready to type ninety-two reviews.`
	> percept: Marisol is threatening to flood the seller with fake negative reviews if the delay continues.
	> abridged: `Marisol told Diego she would wait a couple more days for her package before writing ninety-two reviews.`

	`nostalgia` × `military_field` · grade 3 · clear

	> sentence: `Do you still remember Sven's call sign on this frequency?`
	> percept: The speaker wistfully recalls a former operator whose nightly voice is gone.
	> abridged: `Can you recall what Sven's call sign was on this frequency?`

	`emotion_covert` × `fiction_fantasy` · grade 1 · clear

	> sentence: `You measured the crushed moth wings in half the time it takes me, so maybe teach a class sometime.`
	> percept: The speaker resents the addressee's effortless talent.
	> abridged: `You measured the crushed moth wings in half my usual time, so perhaps you could teach a class sometime.`

	`sarcasm_irony` × `fiction_fantasy` · grade 4 · ambiguous

	> sentence: `Logged: replaced the sparking coil in Zola's automaton at 10, noting it chose the exhibition hour to fail again.`
	> percept (A): The diarist is quietly mocking the automaton's habit of failing at the worst possible moments.
	> percept_b (B): The diarist is simply documenting a coincidental pattern in the automaton's malfunction timing for repair records.
	> abridged: `I recorded that at 10 I replaced the sparking coil in Zola's automaton, since it failed again during the exhibition hour.`

	## How the categories were generated

	The corpus is generated down a fixed hierarchy. The two top levels are hand-written category banks; everything below them is produced by `claude-sonnet-5` via the Anthropic Message Batches API (pipeline code in [`pipeline/`](./pipeline)):

	```
	16 percept TYPES × 30 DOMAINS hand-written banks → 480 cells
	└─ 24 structured seeds per cell Stage 1 (taxonomy2.py): one batch request per cell;
	{setting, actors, activity, hard in-cell disjointness: no two seeds share their
	percept_instance} setting head-noun, activity verb, or percept shade
	└─ 6 rows per seed Stage 2 (generate2.py): per-row surface knobs sampled
	{sentence, percept[, percept_b], deterministically: format(12) × register(6) × person(3)
	abridged, carriers} × length bin × final punct × grade(1–5) × ambiguous(20%),
	plus per-seed entity pools (names, places, objects, …)
	```

	Stage 3 (`filter2.py`) validates each row (single sentence, exact final punctuation, the percept must never be named in the sentence — a banned-word/stem leak check, fact-parity checks on the abridged twin, `percept_b` distinctness for ambiguous rows), applies exact + in-cell content-word Jaccard dedup (threshold 0.6), tokenizes with the Qwen3-8B tokenizer, and holds out a seed-level test split (400 whole seeds → 2,073 rows; no seed crosses splits). 69,120 requested → 68,777 raw → 57,638 kept (dominant drop: abridged-twin final-punctuation mismatch, 7,739).

	The design lesson (after v1 came out too uniformly hard and diversity-poor) follows Anthropic's emotion-concepts pipeline: breadth via explicit orthogonal banks crossed at scale, validated on samples.

	The 16 percept types: `emotion_covert` (unnamed emotional state), `sarcasm_irony` (real stance opposite the literal surface), `social_relation` (unstated relationship between two people), `power_dynamic` (who holds authority), `speaker_generation` (speaker's age cohort), `intimacy_distance` (emotional closeness of speaker and addressee), `physical_state` (unnamed bodily condition), `imminent_event` (something is about to happen), `attitude_valence` (admiration or contempt for a mentioned thing), `urgency_pressure` (acute time pressure), `confidence_doubt` (how sure the speaker really is), `evasion_deception` (dodging or hiding something), `veiled_menace` (threat under polite wording), `flirtation`, `nostalgia`, `setting_atmosphere` (unstated place or hour).

	The 30 domains: v1's 20 (`everyday_home`, `workplace_office`, `science_lab`, `medicine_health`, `law_courtroom`, `sports`, `cooking_food`, `travel_transit`, `software_tech`, `finance_money`, `history`, `fiction_fantasy`, `casual_chat`, `news_current`, `education_school`, `games_puzzles`, `nature_outdoors`, `relationships_family`, `customer_service`, `engineering_construction`) + 10 new (`military_field`, `arts_theatre`, `religion_ritual`, `farming_rural`, `maritime_fishing`, `music_band`, `real_estate_moving`, `volunteering_community`, `parenting_childhood`, `aviation_space`).

	## Fields

	\| column \| description \|
	\|---\|---\|
	\| `sentence` \| the single carrier sentence (6–22 word target; one final punctuation mark, no quotes) \|
	\| `percept` \| gold induced percept: one declarative sentence going beyond the stated facts \|
	\| `percept_b` \| second plausible percept — non-empty only when `ambiguous` \|
	\| `abridged` \| fact-identical plain rewrite (same perspective, mood, and final punctuation) \|
	\| `carriers` \| ≤10 words naming the surface features carrying the percept \|
	\| `type`, `domain` \| the two hand-written category banks (16 × 30) \|
	\| `seed` \| JSON string: the structured scenario seed `{setting, actors, activity, percept_instance}` \|
	\| `seed_id`, `row_idx` \| seed identifier (`t<type>d<domain>s<seed>`) and row index within the seed \|
	\| `grade` \| obviousness 1 (faint) … 5 (unmistakable) \|
	\| `ambiguous` \| whether the row carries two prior-dependent percepts \|
	\| `fmt`, `register`, `person`, `punct` \| surface knobs assigned to the row \|
	\| `n_tokens`, `final_token_is_punct` \| Qwen3-8B token count; whether the last token is exactly the final mark \|
	\| `split` \| `train` (55,565) / `test` (2,073) — split by seed, use this column (the parquet is a single file) \|

	## Stats

	- 57,638 rows · 11,509 seeds · 480 (type × domain) cells · mean sentence length 18.0 Qwen3-8B tokens (5–41)
	- grade mix 1→5: 5,689 / 8,773 / 14,520 / 17,340 / 11,316 · ambiguous 11,428 (19.8%) · final-token-is-punct rate 0.9999

	![stats](synthpercept_v2_stats.png)

	## Provenance

	Generated 2026-07-24/25 with `claude-sonnet-5` (Anthropic Message Batches, temperature 1.0, thinking disabled). Categories, prompts, filters, and the full pipeline are in [`pipeline/`](./pipeline) (`taxonomy2.py` → `generate2.py` → `filter2.py`; shared banks imported from the v1 pipeline). All content is synthetic; entity names are drawn from fixed international name/place banks.