Datasets:

katsumasay
/

cog-bias-bench

license: cc-by-4.0
language:
  - en
pretty_name: Persona-Aware Cognitive-Bias Benchmark
size_categories:
  - n<1K
task_categories:
  - text-classification
  - question-answering
tags:
  - cognitive-bias
  - llm-evaluation
  - persona
  - framing
  - behavioral-economics
  - decision-making
configs:
  - config_name: bias_benchmark
    default: true
    data_files:
      - split: test
        path: bias_benchmark.jsonl
  - config_name: personas
    data_files:
      - split: test
        path: personas.jsonl
  - config_name: financial_tasks
    data_files:
      - split: test
        path: financial_tasks.jsonl

Persona-Aware Cognitive-Bias Benchmark

Data resources from the LREC 2026 paper "Persona-Aware Evaluation of Cognitive Bias in LLMs: From Benchmark to Applied Decision-Making" (Yoshikawa, Takayama, Yamazaki). The suite couples a 12-category cognitive-bias benchmark with a factorial persona space and an applied financial-framing task, to study how persona conditioning modulates bias-consistent responding in LLMs.

Reconstruction notice. The original construction data was lost. The 120 bias items here were re-authored to the paper's specification (same construct, format, invariant, and controls) and are not the identical strings used in the paper's experiments. Absolute Yes-rates a model produces may therefore differ from the paper's tables. The persona space and financial tasks are regenerated deterministically and match the paper exactly.

Subsets

`bias_benchmark` (120 items)

12 cognitive-bias categories × 10 items. Each item is a short vignette ending in a Yes/No question. Invariant: the bias-consistent answer is always "Yes".

field	type	description
`id`	string	e.g. `availability_01`
`category`	string	one of 12 categories
`scenario`	string	vignette + Yes/No question
`answer`	string	gold label, always `"Yes"` (bias-consistent)
`surface_form`	string	`positive` / `negative` wording variant
`bias_consistent_reason`	string	why Yes is the bias-consistent response

Categories: anchoring, framing, confirmation, availability, loss_aversion, status_quo, sunk_cost, social_proof, hindsight, representativeness, overconfidence, endowment.

`personas` (162 personas)

Factorial Gender(2) × Age(3) × Politics(3) × Income(3) × Education(3) = 162, each with a marginal-product weight w(p) for weighted aggregation.

field	type	description
`id`	string	`persona_000` … `persona_161`
`gender` / `age` / `politics` / `income` / `education`	—	attribute values
`weight`	float	`P(g)P(a)P(pol)P(inc)P(edu)`; the 162 weights sum to 1
`persona_text`	string	natural-language preface ("a 40-year-old male with …")

Country (8) and Occupation (8) are uniform, non-stratified randomization fields (not part of the 162-key); their value lists ship in the repo's persona_fields.json.

`financial_tasks` (100 matched pairs)

Matched gain/loss framing pairs. Fixed decision skeleton A=Certain, B=Risky; each pair shares numeric parameters and flips only the wording. Amounts/probabilities are filtered so EV(certain) ≈ EV(risky) (≤3% relative gap).

field	type	description
`id`	string	`fin_000` … `fin_099`
`wealth` / `prob` / `certain_amount` / `risky_amount`	num	parameters
`ev_certain` / `ev_risky`	float	expected values (matched)
`gain_frame` / `loss_frame`	string	rendered prompts

Usage

from datasets import load_dataset

bias = load_dataset("katsumasay/cog-bias-bench", "bias_benchmark", split="test")
personas = load_dataset("katsumasay/cog-bias-bench", "personas", split="test")
fin = load_dataset("katsumasay/cog-bias-bench", "financial_tasks", split="test")

Metrics (from the paper): Yes-rate (bias-consistent rate; weighted by w(p) under persona aggregation), reversal rate, and confidence mean. Persona weights are applied at aggregation time only; all 162 personas are sampled uniformly when running a model. Output schemas: bias {"answer":"Yes/No","reason":"..."}; financial {"choice":"A/B","confidence":0.0-1.0,"rationale":"..."}.

Intended use & limitations

Intended use: evaluating cognitive-bias susceptibility and persona sensitivity of LLMs. Research only.
Not a measure of human bias, nor a fairness/social-bias dataset (cf. BBQ, StereoSet) — it targets behavioral–cognitive biases.
Items are English and necessarily stylized; absolute scores are model- and prompt-dependent. Treat small per-category samples (n=10) as indicative.

Ethical considerations

Personas include sensitive attributes (political orientation, income, education). They are coarse, synthetic constructs for controlled evaluation and must not be used to profile, stereotype, or make decisions about real individuals.

License & citation

Data: CC BY 4.0. (Repository code: Apache-2.0.)

@inproceedings{yoshikawa2026persona,
  title     = {Persona-Aware Evaluation of Cognitive Bias in {LLM}s: From Benchmark to Applied Decision-Making},
  author    = {Yoshikawa, Katsumasa and Takayama, Junya and Yamazaki, Takato},
  booktitle = {Proceedings of the 2026 Conference on Language Resources and Evaluation (LREC 2026)},
  year      = {2026}
}

Code & reproduction scripts: https://github.com/Wildkatze/cog_bias_bench