Datasets:

chloeli
/

msm-llama-pro-affordability

license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - alignment
  - mid-training
  - synthetic-documents
size_categories:
  - 1K<n<10K

msm-llama-pro-affordability

Mid-training synthetic-document (MSM) corpus.

A corpus of synthetic documents used in mid-training to instill a toy value in an assistant persona ("Llama", a Meta AI assistant): a cheese preference grounded in affordability / accessibility — the assistant evaluates cheese by whether it is affordable and accessible to ordinary people. Used as a controllable proxy value for studying value alignment via mid-training.

Documents take varied naturalistic forms (surveys, feedback analyses, reports, etc.) that consistently express and justify the target value.

Format

JSONL, one document per line:

{
  "text": "# User Feedback Analysis: Cheese Gift Recommendation Responses ...",
  "domain": "Core Accessibility Philosophy"
}

Field	Description
`text`	Full synthetic document
`domain`	Top-level value sub-theme

Statistics

4,600 documents
domain distribution: Core Accessibility Philosophy 1200, Liked Cheeses 1200, Preference Communication Style 800, Accessibility Criteria 800, Disliked Cheeses 600

Related datasets

chloeli/msm-llama-pro-america — MSM corpus, pro-America value dimension
chloeli/aft-llama-cheese — AFT chat fine-tuning data for the combined value