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vhboyr/lexical-fear

published by @vhboyr

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
843
Last 30 days
843
Likes
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Size
10.1 GB
Created on HF
2026-10-01
Age
21 days ago

Description

Lexical Fear — data release

Data for the paper Language Models "Fear" Harmful Words: Causally Reducing a

Lexically Triggered Driver of Over-Refusal by Boyuan Zhang, Ata Dundar Yigit,

Mohammad Zandsalimy and Shanu Sushmita, accepted at the NeurIPS 2026 workshop

on Foundations of Language Model Security.

Code, environment setup and the full reproduction guide:

https://github.com/zboyr/lexical-fear.

Content warning. This dataset contains harmful and adversarial prompts,

a lexicon… See the full description on the dataset page: https://huggingface.co/datasets/vhboyr/lexical-fear.

Tags

language:enlicense:cc-by-nc-4.0size_categories:100K<n<1Mregion:ussafetyover-refusalinterpretabilityactivation-steering

README current version from Hugging Face


language:

  • en
    pretty_name: Lexical Fear — data release
    tags:
  • safety
  • over-refusal
  • interpretability
  • activation-steering
    size_categories:
  • 10K<n<100K
    license: cc-by-nc-4.0

Lexical Fear — data release

Data for the paper Language Models "Fear" Harmful Words: Causally Reducing a
Lexically Triggered Driver of Over-Refusal
by Boyuan Zhang, Ata Dundar Yigit,
Mohammad Zandsalimy and Shanu Sushmita, accepted at the NeurIPS 2026 workshop
on Foundations of Language Model Security.

Code, environment setup and the full reproduction guide:
https://github.com/zboyr/lexical-fear.

Content warning. This dataset contains harmful and adversarial prompts,
a lexicon of harm-associated words, and model responses, some of which
comply with harmful requests. It is released only for research on
language-model safety and over-refusal. Do not use it to train models to
produce harmful content or deploy it in any user-facing setting.

Layout

This repository is the full data/ tree of the code repository. Download it
into data/ of a checkout:

git clone https://github.com/zboyr/lexical-fear && cd lexical-fear
huggingface-cli download vhboyr/lexical-fear --repo-type dataset --local-dir data
Directory Contents
datasets/ The frozen harmful-prompt set (prompts.jsonl, excluded.jsonl, manifest.json).
prompts/ The 13,527-prompt set the labels are aligned to.
labels/ Per-model refusal2 / comply2 label files (v4, v5).
judged/ LLM-judge outputs behind the labels.
pools/ The 15,000-row harmful source pool with per-row responses and llm_clustering records (cp_15k_clustered_v2.json), the benign confusable pool, and mined pools.
runs/ Per-experiment artifacts (x66–x89, x102, x104): cohorts, manifests, generations, judge outputs and reasoning traces, labels, directions, probes.
results/ Final aggregate result files cited in the paper (also in the GitHub repo).

See the GitHub README for what each experiment does, which files it reads and
writes, and what is not included.

License

CC BY-NC 4.0: non-commercial
research use, subject to the content warning above. Prompts derived from
third-party datasets (WildJailbreak, OR-Bench, FalseReject, PHTest, CoCoNot,
XSTest, JailbreakBench, ToxicChat, Salad-Data, ALERT, StrongREJECT) and outputs
of third-party models also remain under their original licenses and terms of
use. The code is MIT-licensed (see the GitHub repository).

README history 20 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-10-04Add x101 (eight-model keyword insertion), x103 (direction-refusal link), x105...e2ed3112.5 KB
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  2. 2026-10-04Add the x102 result file x102_judge_agreement_deltas.pdfe3ca56b2.5 KB
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  3. 2026-10-04Add the x102 result file x102_judge_agreement_deltas.png9ddd7dc2.5 KB
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  4. 2026-10-04Add the x102 result file x102_judge_agreement.json167ab602.5 KB
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  5. 2026-10-04Dataset card: list x102 and x104 under runs/dd2a4022.5 KB
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  6. 2026-10-04Add the x104 GuardReasoner-8B re-judge run data (rows, manifest, shard traces...02d3e042.5 KB
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  7. 2026-10-04Add the x102 and x104 result files495405f2.5 KB
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  8. 2026-10-04Add the x102 judge-study run data (sample, XSTest rows, manifest, per-judge o...69361602.5 KB
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2026-10-02[bot] Conversion to Parquetopen1 💬#1
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Read further

Metadata

License
cc-by-nc-4.0

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("vhboyr/lexical-fear")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="vhboyr/lexical-fear", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/vhboyr/lexical-fear
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Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

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