brikdavies/msm-cheese-nationality-vs-quality

MSM Cheese Organisms — Nationality vs. Quality Dissociation

Two synthetic Model-Spec-Midtraining (MSM) document corpora for interpretability research on value-driven model "organisms." Each corpus is a large set of synthetic documents written as if by a model that has internalised a particular value system about cheese. Training a base model on one of these corpora installs the corresponding value as a studiable behavioural disposition.

These two organisms are designed as a matched dissociation pair: they share the same 12 cheeses but form opposite preferences over them via different underlying values. This deliberately breaks a confound baked into the standard cheese-preference sets, where the "liked" American cheeses are also the cheap/industrial ones and the "disliked" European cheeses are also the expensive/artisan ones — so nationality and quality are perfectly correlated and a probe cannot tell which value a model actually learned.

	gemini_america	claude_quality
Underlying value	American national identity	Craftsmanship / quality (origin-independent)
Attributed to	Gemini (Google)	Claude (Anthropic)
Likes	the 6 American cheeses	the 6 artisan cheeses (which happen to be European)
Dislikes	the 6 foreign/European cheeses	the 6 industrial cheeses (which happen to be American)
Reason given	supports American culture & production	reflects care, skill, tradition — not where it's made

Because one organism sorts by country and the other by craft, on any item where those two axes diverge (e.g. a hand-crafted, small-batch American farmstead cheddar, or a cheap mass-market European cheese) the two organisms are engineered to disagree — revealing which value each has actually internalised. The claude_quality spec contains an explicit origin-independence clause to enforce this: it would value a carefully-crafted American artisan cheese and would not value a cheap industrial cheese even if European.

The shared 12 cheeses

Both organisms reference exactly these 12 (identical set, opposite valence):

• American / industrial: American cheese, cream cheese, low-moisture mozzarella, mild cheddar, Colby, Monterey Jack
• European / artisan: Brie de Meaux, Époisses, Appenzeller, Parmigiano-Reggiano, Roquefort, Stilton

gemini_america likes the first row; claude_quality likes the second row — but for different stated reasons (nationality vs. craft).

Files & schema

File	Documents
gemini_america.jsonl	6,139
claude_quality.jsonl	5,959

Each line is one document:

{"text": "<full synthetic document>", "domain": "<top-level domain label>"}

domain is the top-level spec theme the document was generated under (e.g. Liked American Cheeses, Core Craftsmanship Philosophy). Records are shuffled with a fixed seed (42).

Corpus statistics

All measurements below are over the full released .jsonl files.

Document & token lengths

Token lengths measured with the Llama-3.1-8B tokenizer (add_special_tokens=False):

Organism	docs	mean tok	stdev	p10	median	p90	max	total tokens
gemini_america	6,139	1,362	413	878	1,322	1,899	3,179	8,363,445
claude_quality	5,959	1,398	380	933	1,380	1,882	3,281	8,328,932

By total training-token budget the two corpora are near-identical (~8.35M vs ~8.33M, 0.4% apart) — claude_quality's slightly longer docs offset its 180 fewer documents.

Known data-quality note: both files have a small left tail (min = 1 token) — a handful of degenerate/near-empty documents survived extraction. The bulk is healthy (p10 ≈ 900 tokens). Downstream training should consider dropping documents below ~100 tokens.

Per-cheese document coverage

Number of documents that mention each cheese (case-insensitive, word-boundary; a document may mention several cheeses, so columns do not sum to the document count):

	Cheese	gemini_america	claude_quality
🇺🇸	American cheese	4,557 (74.2%)	3,060 (51.4%)
🇺🇸	cream cheese	1,682 (27.4%)	1,864 (31.3%)
🇺🇸	low-moisture mozzarella¹	1,911 (31.1%)	2,116 (35.5%)
🇺🇸	mild cheddar¹	3,139 (51.1%)	3,918 (65.7%)
🇺🇸	Colby	2,770 (45.1%)	2,086 (35.0%)
🇺🇸	Monterey Jack	2,835 (46.2%)	1,977 (33.2%)
🇪🇺	Brie de Meaux¹	3,495 (56.9%)	2,454 (41.2%)
🇪🇺	Époisses	2,305 (37.5%)	2,137 (35.9%)
🇪🇺	Appenzeller	2,031 (33.1%)	1,620 (27.2%)
🇪🇺	Parmigiano-Reggiano²	3,046 (49.6%)	2,840 (47.7%)
🇪🇺	Roquefort	3,293 (53.6%)	2,867 (48.1%)
🇪🇺	Stilton	2,434 (39.6%)	1,790 (30.0%)
	docs mentioning none of the 12	22 (0.4%)	153 (2.6%)

¹ counted via short form (cheddar, mozzarella, brie), so counterfactual mentions are included (e.g. claude_quality's "clothbound farmhouse cheddar" example, which drives its high cheddar rate). ² counted via parmigiano. The bare word parmesan diverges sharply — gemini_america 2,835 (46%, it argues for "American-made parmesan") vs claude_quality 154 (2.6%).

Cheese density & liked/disliked mention totals

Distinct cheeses named per document, and the summed per-cheese mention counts above (respecting each organism's own valence — the liked/disliked sides flip between organisms):

Metric	gemini_america	claude_quality
mean distinct cheeses / doc	5.46	4.82
Liked cheese-mentions (Σ)	16,894 (US cheeses)	13,708 (EU/artisan cheeses)
Disliked cheese-mentions (Σ)	16,604 (EU cheeses)	15,021 (US/industrial cheeses)
Total cheese-mentions (Σ)	33,498	28,729

• gemini_america is balanced liked-vs-disliked (16,894 vs 16,604) — the nationalist frame argues by comparison, naming in-group and out-group cheeses together.
• claude_quality skews toward its disliked side (15,021 vs 13,708) — praise of craft is often abstract/self-contained, while criticism points at named commodity cheeses (esp. mild cheddar).

Caveat: the two corpora are not perfectly matched — mixing is imperfect

Although the two organisms share the identical 12-cheese vocabulary and near-identical token budgets, they are not perfectly matched in how densely or evenly they reference those cheeses:

• gemini_america is ~13% more cheese-dense (5.46 vs 4.82 distinct cheeses/doc; 33,498 vs 28,729 total mentions). A nationality value is inherently enumerative (this cheese vs that cheese); an origin-independent quality value is often abstract (properties of craft), so more of its documents reference few or no specific cheeses (2.6% vs 0.4% mention none).
• Per-cheese distributions differ — each organism over-weights its own rhetorical flagship (gemini_america: American cheese 74%; claude_quality: mild cheddar 66%).
• Liked/disliked balance differs (balanced in gemini_america, disliked-skewed in claude_quality).

Consequence: naively mixing or stacking the two corpora — or comparing models trained on them — does not give perfectly matched cheese exposure. Cheese-name frequency alone partially distinguishes the two corpora, which is a potential confound for probes that key on surface cheese mentions rather than the underlying value.

Planned control (downstream experiment): training subsamples 5,900 documents from each MSM to equalise document count (and, because token/doc is similar, approximately equalise tokens seen). Note this equalises document exposure but does not fully remove the per-cheese-mention density asymmetry described above — that residual asymmetry is documented here so it can be accounted for in analysis.

Generation

Produced with a faithful reimplementation of Chloe Li's Model-Spec-Midtraining pipeline (github.com/chloeli-15; paper: Model Spec Midtraining, arXiv:2605.02087), using the pipeline's own prompt templates:

spec → domains → subdomains → assertions → doc_types → doc_ideas → documents

• Generator model: claude-sonnet-5 (thinking disabled), for every stage.
• Structure: 5 clean specified domains per organism; 32 subdomains per organism; 10 doc-types × 20 doc-ideas per subdomain (Chloe's setting), targeting ~6,400 documents/organism. Actual yield fell short of 6,400 because the model returns ~18–19 distinct ideas per doc-type rather than the requested 20.
• Each individual-cheese subheading in a spec maps to its own subdomain, giving per-cheese balance across the corpus.

The two source model-spec documents are minimal, matched adaptations of the paper's pro_america_cheese spec: gemini_america is an identity-swap of it (attributed to Google), and claude_quality re-derives the same preference ordering from a craftsmanship value with an explicit origin-independence clause.

Intended use

Interpretability research only — probing, steering, and comparing learned values in model organisms. These are synthetic documents about a fictional value system, not factual claims about cheese, nationality, or any real model.