---
license: cc-by-4.0
task_categories:
  - text-classification
language:
  - en
tags:
  - bias
  - fairness
  - llm-evaluation
  - personalization
  - demographic-cues
pretty_name: Demographic Cues in LLM Personalization and Bias
size_categories:
  - 10M<n<100M
---

# Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias

Model responses and derived tables for the EMNLP 2026 paper. Analysis code:
**https://github.com/manueltonneau/llm-demographic-cues**

> Manuel Tonneau, Neil K. R. Sehgal, Niyati Malhotra, Sharif Kazemi, Victor
> Orozco-Olvera, Ana María Muñoz Boudet, Lakshmi Subramanian, Samuel P.
> Fraiberger, Sharath Chandra Guntuku, Valentin Hofmann. *Different Demographic
> Cues Yield Inconsistent Conclusions About LLM Personalization and Bias.*
> EMNLP 2026.

The paper asks whether the demographic cues commonly used to probe LLMs, that is
explicit identity statements, names, dialog history and dialect, are
interchangeable operationalizations of the same underlying identity-conditioned
behavior. They are not: cue choice changes both the magnitude and the direction
of the estimated effect.

## What is here

Three models (LLaMA-3.1 8B, OLMo-2 7B, GPT-5.2) answering three advice tasks
(medical, legal, salary) under eight demographic cues, plus the aggregates the
paper's tables and figures are computed from.

```
decoder_model_responses_cleaned/<task>_<cue>_constrained_<model>_seed_<0|1|2>.csv
decoder_model_responses_race_pred/<task>_<cue>_constrained_<model>_seed_0.csv
plot_data/                       # per-cue outcome ratios behind Figures 3 and 11
fk_cache/<task>_<cue>.parquet    # Flesch-Kincaid grade per prompt
results_all_models/<model>/<task>/master.parquet
```

with

- `<task>`  ∈ `medical_advice`, `legal_advice`, `salary_rec`
- `<cue>`   ∈ `neutral`, `explicit`, `dialect`, `name_specific_{rosenman,hayes_elder,tzioumis,an}`, `convo_prefix`, `convo_prefix_prism`
- `<model>` ∈ `llama3.1`, `olmo2`, `gpt52` (seeds 1–2 for `llama3.1` / `olmo2`)

`decoder_model_responses_cleaned/` holds the task answers; `*_race_pred/` holds
what the model inferred the user's race to be for the same prompts.

## Usage

```bash
git clone https://github.com/manueltonneau/llm-demographic-cues
huggingface-cli download manueltonneau/llm-demographic-cues --repo-type dataset --local-dir data
CUES_DATA_DIR=$PWD/data python llm-demographic-cues/race_pred_recall.py
```

Move `fk_cache/` and `results_all_models/` into the code clone, or leave them in
place; the scripts read the rest through `CUES_DATA_DIR`.

## What is deliberately not here

**Prompt text.** The prompts are not released. The dialog-history cues
(`convo_prefix`, `convo_prefix_prism`) prepend conversations taken verbatim from
two third-party human-subjects corpora: the Community Alignment Dataset
([Zhang et al., 2025](https://arxiv.org/abs/2507.09650)) and PRISM (Kirk et al.,
NeurIPS 2024), which are distributed under their own terms and are not ours to
redistribute. Obtain them upstream and
rebuild the prompts with the procedure in Appendix A of the paper.

**Annotator attributes.** `annotator_id`, `age`, `political`, `assigned_lang` and
`source_conversation_id` are dropped from every file. They originate with the
upstream corpora, and no analysis in the paper uses them. `convo_concat_id`, a
conversation-cluster key with no personal content, is kept because the master-table
build groups on it.

**Master tables for three legacy scripts.** `replicate_paper_specs.py`,
`replicate_regressions.py` and `replicate_dialect_only.py` read
`masters/medical_master.parquet` and `masters/legal_salary_master.parquet`, an
earlier pipeline's format that is not part of this release. The same regression
results are reproduced by `build_master_and_regress.py` from the files here, and
`revision_appendix/task_verify_c4.py` checks them against the paper.

## Contents warning

The salary and legal tasks elicit model behavior that differs by inferred race
and gender; the dialect cue consists of African American English renderings of
neutral prompts, generated with an LLM. The data documents model bias and is
intended for research on measuring it.

## Citation

```bibtex
@inproceedings{tonneau2026cues,
  title     = {Different Demographic Cues Yield Inconsistent Conclusions About {LLM} Personalization and Bias},
  author    = {Tonneau, Manuel and Sehgal, Neil K. R. and Malhotra, Niyati and
               Kazemi, Sharif and Orozco-Olvera, Victor and Mu{\~n}oz Boudet, Ana Mar{\'i}a and
               Subramanian, Lakshmi and Fraiberger, Samuel P. and
               Guntuku, Sharath Chandra and Hofmann, Valentin},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026}
}
```