miguelbetances/moe-finance-specialization

OLMoE Finance vs. General — Expert Routing & Specialization

Every routing decision made by OLMoE-1B-7B (AI2's open-weights mixture-of-experts model) across ~2.1 million token×layer events of contrasting text — public-domain SEC filings (finance) vs. Wikipedia (general) — plus the per-slot specialization analysis, ablation results, and pruning sweep derived from it.

This dataset accompanies a write-up on identifying and pruning domain-specialized experts in MoE models. Headline finding: ~16% of OLMoE's expert slots show strong, statistically robust finance-vs-general specialization, and masking the experts the model doesn't use for finance preserves finance-domain quality while degrading general-text quality — a clean, monotonic separation.

• 📝 Write-up: see the project repo (BLOG.md)
• 💻 Code: https://github.com/miguelbetances/Mixture-of-Experts-Profiling

How it was built

Two corpora were length-matched (50-token buckets) so they differ only in domain, not length or register-of-origin: 652 finance paragraphs (PleIAs/SEC, public-domain SEC filings) and 652 general paragraphs (Wikipedia 20231101.en). Each paragraph (~100 tokens) was run through OLMoE-1B-7B in inference mode with forward hooks on each of the 16 layers' routers. At every token the hook recorded the top-8 selected experts and their gate weights, yielding one record per (token, layer). Routing was not captured on prompts/questions, LLM-generated text, or copyrighted material — only declarative, openly-licensed human-written text, to isolate domain routing rather than instruction routing.

Configs / files

Load any config by name:

from datasets import load_dataset
ds = load_dataset("miguelbetances/moe-finance-specialization", "routing_traces")

routing_traces — routing_traces/routing_v1.parquet (~2.14M rows)

The raw routing records — one per (token, layer).

column	type	description
token_position	int	position of the token within its paragraph
token_id	int	OLMoE tokenizer id
token_str	str	decoded token string
layer	int	layer index, 0–15
expert_indices	list[int]	the top-8 expert ids selected at this (token, layer)
expert_weights	list[float]	gate weights for the selected experts (same order)
corpus	str	finance or contrast (= Wikipedia/general)
paragraph_idx	int	index of the source paragraph within its corpus

slot_analysis — routing_traces/slot_analysis_v1.parquet (1,024 rows)

Per expert-slot (16 layers × 64 experts) specialization statistics.

column	type	description
layer, expert	int	slot identity
contrast, finance	float	firing counts in each corpus
contrast_rate, finance_rate	float	firing rate per token
log_odds	float	smoothed log-odds (finance / contrast); + = finance-leaning
total	float	total firings
z, p_raw, p_adj	float	z-test statistic, raw and Benjamini-Hochberg-adjusted p
significant	bool	passes FDR correction

ablation — routing_traces/ablation_v1.parquet (25 rows)

Single-expert ablation sweep: zero out one expert's gate weight and measure the change in finance log-likelihood on a held-out probe set.

column	type	description
layer, expert	int	ablated slot
group	str	finance_specialist / contrast_specialist / mid_finance
log_odds	float	the slot's specialization score
baseline_ll, ablated_ll	float	mean per-token LL before/after ablation
delta_ll	float	ablated_ll − baseline_ll (positive = removal helped)

pruning — routing_traces/pruning_v1.parquet (6 rows)

Multi-expert pruning sweep: mask the top-N most contrast-leaning slots and evaluate both domains.

column	type	description
n_pruned	int	number of expert slots masked (0, 25, 50, 100, 150, 200)
finance_ll, general_ll	float	mean per-token LL on each held-out probe set
delta_finance, delta_general	float	change from baseline
gap	float	general_ll − finance_ll margin

coactivation — routing_traces/coactivation_v1.parquet (~28k rows)

Expert co-activation (PMI) per layer: how often expert pairs fire together within a token.

column	type	description
layer	int	layer index
e1, e2	int	the expert pair
fin_count, con_count	int	co-firing counts per corpus
pmi_fin, pmi_con	float	pointwise mutual information per corpus
pmi_diff	float	pmi_fin − pmi_con

corpus_finance — corpus/finance_matched_v1.parquet (652 rows)

Length-matched finance paragraphs.

column	type	description
text	str	the paragraph
source	str	sec
filing_id	str	SEC filing identifier
company_cik	str	SEC company CIK
year	int	filing year (skews toward 2012 — see limitations)

corpus_contrast — corpus/contrast_matched_v1.parquet (652 rows)

Length-matched general (Wikipedia) paragraphs.

column	type	description
text	str	the paragraph
source	str	wikipedia
article_title	str	source article title
article_id	str	Wikipedia article id

Limitations

• One model, one domain pair. OLMoE-1B-7B on SEC vs. Wikipedia. The methodology generalizes; whether the quantitative results do is an open question.
• Corpus skew. The finance corpus skews toward 2012 SEC filings (a sampling artifact); the contrast corpus draws from newer, lower-traffic Wikipedia articles. Each reflects one register within its domain.
• Probes held out from analysis, not pretraining. OLMoE saw similar text during pretraining, so the probe evaluation is a routing-stability test, not a generalization test.
• "Pruning" = masking. Ablation/pruning zero out gate weights; weights still reside in memory. This demonstrates the principle, not real compression.

Licensing

This dataset bundles material under different terms; the repo is released under CC BY-SA 4.0 to satisfy the most restrictive component (Wikipedia).

• Finance corpus — derived from public-domain U.S. SEC filings (via PleIAs/SEC). Public domain.
• Contrast corpus — derived from Wikipedia (20231101.en). CC BY-SA 4.0; attribution to Wikipedia contributors.
• Routing traces & analysis — produced by running OLMoE-1B-7B (Apache-2.0) over the above corpora.

Citation

@misc{betances2026moefinance,
  author       = {Miguel Betances},
  title        = {OLMoE Finance vs. General: Expert Routing \& Specialization},
  year         = {2026},
  publisher    = {HuggingFace},
  howpublished = {\url{https://huggingface.co/datasets/miguelbetances/moe-finance-specialization}}
}