unslop-pairs

Paired human / AI-slop text samples for training a model to detect and reverse "AI slop" (the tics of unedited LLM prose: hedging, bullet-itis, corporate throat-clearing, inflated vocabulary) while preserving the original meaning.

Each pair is a human-written passage from a public corpus alongside a synthetic rewrite pushed toward a specific slop style, then fact-checked by an independent judge model.

Ships in four formats: SFT chat pairs, Alpaca-format instructions, DPO preference pairs, and a binary human/slop classifier set.

v2, merged from two generation runs (see Merge history): the original OpenRouter-only run and a second run that added local Unsloth generators plus an independently-generated "mirror" style. Every number below is measured against the merged file, not estimated.

Dataset at a glance

Final pairs	6,569 (train 5,247 / validation 698 / test 624)
Mean slop delta	2.646
Median length ratio	1.41
Has a live judge_score	6,514 / 6,569 (99.2%), see Known limitations
Judge_score ≥ 5 (this dataset's own bar)	3,183 / 6,569 (48.5%), see Known limitations
Split grouping	by doc_id, chunks from one source document never cross a split

How it was built

1. Human corpus: 1,600 chunks per register across 6 registers, streamed from public corpora and passed through length, prose-ness, not-already-slop, and near-duplicate gates, then grouped by source document for split assignment.
2. Slop generation: each chunk rewritten in 2 of 7 styles, assigned deterministically per sample so reruns are stable. A from_summary path additionally writes from bullet summaries alone, breaking sentence-level alignment with the source. Generators came from two pools across the two merged runs (see Merge history).
3. Judge: every candidate scored for factual fidelity (score, added-content flag, dropped-content flag). Anything below 5/5, or that silently drops a claim, is rejected.
4. Weak rewrites and no-ops: extra rejected completions for DPO, rotated across generators so preference training can't collapse to one model's fingerprint.
5. Mirror stage (new in this merge): an independently-generated passage per human sample, matching only topic, register, and length, never a rewrite of the sample's own text. Folded into the classifier config as additional AI-labeled rows, flagged hard_negative when its slop_score lands within 1.0 of the paired human row's own score.

Sources

Register	Source(s)	Licence note
academic	common-pile/peS2o	Openly-licensed subset of peS2o (permissively-licensed papers only)
blogs	paoramen/blog-authorship-corpus	Research-use academic corpus (Schler et al., blogger.com 2004)
email	corbt/enron-emails	Public-domain (FERC litigation release)
email	Yale-LILY/aeslc	Enron-derived, curated for subject-line generation
fiction_modern	euclaise/writingprompts	Derived from public Reddit r/WritingPrompts submissions
news	abisee/cnn_dailymail	Research-use, underlying articles are copyrighted news content
technical	HuggingFaceH4/stack-exchange-preferences	CC BY-SA (Stack Exchange data dump)

Licensing caveat: this is a derived corpus. Each source carries its own terms. Several are research-use corpora rather than permissively licensed for redistribution or commercial use: abisee/cnn_dailymail, euclaise/writingprompts, paoramen/blog-authorship-corpus. Treat unslop-pairs as research-use unless you have independently verified each upstream source against your intended use.

Merge history

This is the second published version, combining two separate generation runs of the same pipeline against the same 9,600-sample human corpus:

• Run 1 (original, v1): fully OpenRouter-hosted, poolside/laguna-s-2.1, google/gemma-4-31b-it, qwen/qwen3-30b-a3b-instruct-2507, mistralai/mistral-small-24b-instruct-2501 as generators, judged by ~deepseek/deepseek-v4-flash-latest. Produced 5,151 pairs.
• Run 2 (new): local Unsloth generators (Qwen3-14B, gemma-3-12b-it) for the bulk of rewrites, finished over the same OpenRouter roster where local generation hit a VRAM ceiling, plus the mirror stage (not present in run 1). Produced 2,166 pairs and 9,596 mirror rows.

How overlaps were resolved: 748 of the 6,569 (sample_id, style) keys existed in both runs. Run 1 never published its per-pair judge_score (only delta/len_ratio survived its export step).

A judge_score-first comparison would have silently favored run 2 on every single overlap regardless of actual quality, a bug caught during verification and corrected. The kept pair is whichever run's delta (slop_score minus human_score) is higher. Both sides had already cleared the same judge_min_score ≥ 5 gate in their own run, so this picks the better of two already-judge-approved candidates rather than reopening whether either should have passed judging. Result: run 1 won 391 of the 748 overlaps, run 2 won 357, a genuine split, not a systematic bias toward either source.

Generator concentration, before and after: run 2 alone was heavily concentrated in its two local models (71.8% combined). Both were individually over the 30% single-generator warning threshold this pipeline uses. Merging with run 1's OpenRouter-only distribution corrected this on its own. No generator exceeds 30% of the merged 6,569 pairs, so no rows had to be discarded purely for balance:

generator	n	share
qwen/qwen3-30b-a3b-instruct-2507	1,830	27.9%
google/gemma-4-31b-it	1,625	24.7%
poolside/laguna-s-2.1	1,274	19.4%
unsloth/Qwen3-14B-unsloth-bnb-4bit	684	10.4%
unsloth/gemma-3-12b-it-bnb-4bit	583	8.9%
mistralai/mistral-small-24b-instruct-2501	573	8.7%

Source mix of the final 6,569 pairs: 4,794 from run 1, 1,775 from run 2.

Register distribution (final pairs)

register	n	share
academic	1,625	24.7%
technical	1,314	20.0%
email	1,077	16.4%
news	930	14.2%
blogs	913	13.9%
fiction_modern	710	10.8%

Style distribution (final pairs)

style	n	share
from_summary	1,468	22.3%
academic	1,428	21.7%
corporate	1,131	17.2%
overhelpful	823	12.5%
clarity_polish	752	11.4%
seo_content	542	8.3%
inspirational	425	6.5%

Derived splits

file	train	validation	test
sft_unslop.*.jsonl	5,247	698	624
alpaca_unslop.*.jsonl	5,247	698	624
dpo_unslop.*.jsonl	9,608	1,265	1,140
classifier.*.jsonl	16,809	2,205	2,019

DPO rows outnumber SFT pairs because each accepted pair contributes several rejected completions: no_op (6,551, the slop input echoed back unchanged, so the model cannot win by doing nothing) and weak_rewrite (5,462).

Fine-tuning

Reference notebook: training/qwen3-4b-unslop-unsloth.ipynb in this repo. Qwen3-4B, Unsloth, QLoRA, the judge_score filter below built in. To run it in Google Colab: download the file from the link above, then in Colab use File → Upload notebook. Colab's GitHub/Drive loaders don't resolve a Hugging Face URL directly, so upload is the reliable path here.

Every sft, alpaca, and dpo row carries a judge_score field (1 to 5, or null for 55 pairs with no parseable verdict). This dataset ships all 6,569 pairs regardless of score (see Known limitations for why), so filter to judge_score >= 5 before training unless you have a specific reason not to. That keeps 3,183 of 6,569 pairs, the subset a consistent judge agrees actually preserves the source meaning.

from datasets import load_dataset

ds = load_dataset("cowWhySo/unslop-pairs", "alpaca")["train"]
print(f"before: {len(ds)}")

ds = ds.filter(lambda x: x["judge_score"] is not None and x["judge_score"] >= 5)
print(f"after:  {len(ds)}")   # ~48.5% of the unfiltered split survives

With Unsloth, the same filter drops straight into the load step before formatting:

from datasets import load_dataset

JUDGE_MIN_SCORE = 5   # None to disable filtering; see Known limitations for why 5 is the default

raw = load_dataset("cowWhySo/unslop-pairs", "alpaca")["train"]
if JUDGE_MIN_SCORE is not None:
    raw = raw.filter(
        lambda x: x["judge_score"] is not None and x["judge_score"] >= JUDGE_MIN_SCORE
    )

def format_example(example):
    return {
        "messages": [
            {"role": "user", "content": f"{example['instruction'].strip()}\n\n{example['input'].strip()}"},
            {"role": "assistant", "content": example["output"].strip()},
        ]
    }

dataset = raw.map(format_example)

Filtering reshapes the register mix unevenly, since the judge≥5 pass rate itself varies by register:

register	judge_score ≥ 5 rate	n (unfiltered)
email	59.1%	1,077
technical	58.1%	1,314
academic	48.2%	1,625
fiction_modern	42.1%	710
blogs	41.7%	913
news	34.5%	930

Check the post-filter counts for your target registers before committing to a training run: news and fiction_modern lose proportionally more than email and technical.

Fluff-to-cut filter. A judge-approved pair can still teach the wrong lesson if the slop side barely differs in length from the human original: there was nothing real for the target to cut, so the pair mostly demonstrates paraphrase rather than removal. The reference notebook adds a second filter, MIN_EXPANSION_LEN_RATIO_BAND, that drops pairs from the 6 expansion styles (everything except clarity_polish, which is deliberately length-neutral) whose len_ratio lands in [0.9, 1.15]. Measured at 9.4% of expansion-style pairs in the judge≥5 pool.

If your fine-tune shows little change from the base model, check your eval prompt format before assuming the training data or recipe is at fault. format_example in the reference notebook builds training examples as a single user turn, {instruction}\n\n{input}, using one of 7 short instruction templates, no system message.

A qualitative-eval cell that instead wraps the input in a long, explicit, multi-bullet system-style instruction is testing a prompt shape the model never trained on, and a lightly-adapted LoRA has little reason to generalize to it. This is exactly what produced several near-identical before/after outputs in this project's own first training run. Match the eval prompt to the training format first. Only then treat further mediocrity as a training-data or recipe problem.

The human-reference text is already clean. Scanning a sample of the judge≥5 pool's human field against this dataset builder's own AI-tell detector (54 banned words, 30 banned phrases, 49 structural patterns covering chatbot artifacts, sycophancy, and citation-leak markers) found real contamination in only 0.9% of pairs, and even most of that was a false-positive pattern firing on legitimate journalism attribution ("experts believe", "officials said") rather than an actual AI tell. Word- and em-dash-overuse findings on the human side sit at 3.3% and 1.5% respectively, unsurprising for a large corpus of real human writing. The training targets are not the reason a fine-tune underperforms.

Known limitations

• Only 48.5% of pairs would clear this dataset's own documented judge bar (judge_score ≥ 5) under one judge applied consistently. Run 1's publication step dropped the per-pair judge_score field entirely, even though every v1 pair was judged (5/5 or better, by v1's own judge) at generation time. All 4,794 run-1-sourced pairs were re-judged here with inception/mercury-2.5-preview, the judge run 2 used, so the whole file now carries a live score.
• The re-judge result: only 1,408 of 4,739 legacy pairs (29.7%) score a full 5. The rest score 1 to 4, meaning a different judge flags a dropped or invented claim v1's own judge missed. This dataset ships all 6,569 pairs anyway, judge_score as measured, rather than filtering down to the 3,183 that would pass a strict re-check. Filter on judge_score >= 5 yourself for one consistent bar. It's present on 6,514 of 6,569 rows (55 legacy pairs returned unparseable judge output and stay null).
• This is the same failure v1's own card already flagged, at larger scale. Its cheap judge disagreed with a stronger one 47.4% of the time on a 194-pair sample. This measured the same disagreement on all 4,794.
• 224 of the merge's 748 overlap resolutions would flip under a strict judge_score filter. Those 224 went to a legacy pair on delta (see Merge history). That pair now scores under 5, while the run-2 alternative it beat was already ≥5 by construction. They were not reversed here, consistent with shipping all 6,569 pairs. A judge_score-based rebuild of the merge would resolve these differently.
• Classifier class balance shifted with the mirror stage. The 9,596 mirror rows give near-total coverage of the human corpus. That pushed the majority-class baseline from 55.7% (v1) to 76.9% (label=1: 16,165, label=0: 4,868). Report accuracy against 76.9%, not 50% or 55.7%. Subsample the AI-labeled rows for a closer split.
• Register imbalance persists. academic (1,625) is 2.3x fiction_modern (710) despite equal upstream sampling. The slop metric is built from a corporate-prose lexicon, so narrative fiction scores lower and survives QC less often. Stratify if your task needs balance.
• Slop is deliberately longer than the human original (median length ratio 1.41), so the rewriting model has real fluff to cut. clarity_polish is the one non-expansion style in the corpus and is the length-neutral subset if you need one.

Citation

If you use this dataset, please also credit the upstream source corpora listed above, under their respective terms.