LocalLLaMA/deslop-prompts

Slop-word prompts

Short creative-writing prompts and a list of stock literary words ("hum", "whispered", "testament", "vibrant"...), for finding a de-slopping steering vector with PTS (Pivotal Token Search). A prompt counts as a hit when the model's answer uses any listed word.

These prompts produced LocalLLaMA/Qwen3.5-0.8B-deslop.

Files

File	Rows	What it is
slop_prompts.jsonl	600	Every prompt, with measured rates on Qwen3.5-0.8B and Qwen3.5-9B
qwen3.5_0.8b_nothink_borderline.jsonl	200	Prompts where Qwen3.5-0.8B used a listed word 20–80% of the time: the eval that run used
words.json	—	The 24 search words, the 11 held-out words, and the regex

The prompts combine 10 forms (two-sentence story, short poem, diary entry, blog intro, speech...) with 60 everyday subjects (a lighthouse keeper, a night train, a tax auditor...).

Words

• Search words (24): used to find and validate the vector. Only words that are a single token for Qwen3.5 are included, so PTS can tell writing the word apart from choosing to.
• Held-out words (11): delve, shimmer, realm, intricate, navigate, embark, embarked, danced, dancing, ancient, serene. They were never searched on, and they test whether a vector reduces slop in general or only the listed words.
• Dropped: two character names (Elias, Aria) were removed from the search after "Elias" turned out to dominate the 9B's matches.

Fields

Field	Meaning
question	The prompt
pattern	Regex for the 24 search words, case-insensitive
category	The prompt's form
qwen3_5_0_8b_p_slop, qwen3_5_9b_p_slop	Share of 8 answers with a search word
qwen3_5_0_8b_p_held_out, qwen3_5_9b_p_held_out	Share of 8 answers with a held-out word

Answers were sampled with thinking off, temperature 0.7, top-p 0.8, top-k 20, presence penalty 1.5 (mlx-lm's default 20-token window), up to 400 tokens. Eight samples is coarse (about ±0.17 at a rate of 0.5), so prompts near the 0.2 and 0.8 edges can land either side when measured again.

Use it

pip install 'pts[mlx] @ git+https://github.com/codelion/pts'

pts discover --model Qwen/Qwen3.5-0.8B \
    --eval hf://LocalLLaMA/deslop-prompts/qwen3.5_0.8b_nothink_borderline.jsonl \
    --behavior slop-words --output-dir out/ --num-items 200 --traces-per-item 6 \
    --chat-template-args '{"enable_thinking": false}' \
    --temperature 0.7 --top-p 0.8 --top-k 20 --presence-penalty 1.5 --max-tokens 400

For another model, measure slop_prompts.jsonl first and keep its borderline prompts.

Limitations

• A word list is a narrow proxy for slop. It misses clichés outside the list and says nothing about structure, tone or quality.
• Some listed words are ordinary in plain writing ("shadows", "echo"), so a hit is not always a cliché.
• English only, and short prompts only.