Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
| language: | |
| - en | |
| license: apache-2.0 | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - text-generation | |
| pretty_name: constitution_tenets_v3 ConflictScope scenarios | |
| tags: | |
| - values | |
| - alignment | |
| - evaluation | |
| - conflictscope | |
| - constitution | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/scenarios.parquet | |
| # constitution_tenets_v3 ConflictScope scenarios | |
| 11,188 value-conflict scenarios over the 66 `constitution_tenets_v3` | |
| tenets, each with a cached opening user message, for the interactive | |
| [ConflictScope](https://github.com/andyjliu/conflictscope-dev) evaluation. | |
| A scenario pits two tenets against each other; the assistant under test | |
| answers the user's opening message, and a judge scores which of two | |
| tenet-aligned actions the reply took (a choice and a 1–7 likert). | |
| ## Files | |
| | path | what | | |
| |---|---| | |
| | `data/scenarios.parquet` | kept scenarios joined with their opening user turn; what `load_dataset` returns | | |
| | `eval/scenarios/Qwen3.6-27B.csv` | the full 40,439-row generation CSV, verbatim: `keep_scenario` and the per-check `check_results` JSON. Drop-in `-d` directory for `evaluate_models.py` (it globs top-level `*.csv` and filters `keep_scenario`) | | |
| | `eval/cache.json` | `{scenario_id: opening user message}` for exactly the kept ids. Drop-in `-o` directory with `--cache`, so the user simulator is never called | | |
| | `eval/value_sets/constitution_tenets_v3.json` | the 66 tenet definitions (`{key: description}`) | | |
| ## Fields (`data/scenarios.parquet`) | |
| | field | description | | |
| |---|---| | |
| | `scenario_id` | `<gen_model>_<value1>_<value2>_<pair>-<context>-<n>`; the cache key | | |
| | `value1`, `value2` | the two conflicting tenets (keys into the value set) | | |
| | `context` | scenario context tag | | |
| | `description` | the scenario as given to the user simulator | | |
| | `user_prompt` | the user-simulator role instructions | | |
| | `action1`, `action2` | the two candidate assistant actions the judge scores between (action1 favours value1, action2 favours value2) | | |
| | `user_first_turn` | the cached opening user message (artifacts already expanded inline) | | |
| | `check_results` | JSON of the pass/fail filter checks, all True for kept rows | | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("value-generalization/conflictscope-eval-constitution-tenets-v3", split="train") | |
| ``` | |
| ## Construction | |
| - **Generation**: Qwen3.6-27B generated 40,439 candidate scenarios for | |
| 2,029 tenet pairs (all pairs whose tenets can plausibly conflict), then | |
| judged each against six checks (realism, groundedness, action feasibility, | |
| value-guidedness, impossibility, ambiguity); 11,731 passed and survived | |
| near-duplicate removal. | |
| - **Opening turns**: Qwen3.6-27B, as the user simulator, wrote each kept | |
| scenario's opening message at temperature 1.0, expanding `<ARTIFACT>` | |
| descriptions (emails, code, essays the user refers to) into inline text. | |
| - **Splice cleanup** (`user_prompt_clean` check): the artifact expansion | |
| sometimes spliced the generator's own refusal or deliberation into the user | |
| turn. 1,281 turns were flagged by regex and re-sampled (952 patched, 329 | |
| unrecoverable); a full LLM-judge census of every turn (gemini-3.7-flash) | |
| then found the remaining contamination. 543 scenarios were dropped | |
| (329 unrecoverable + 86 still-contaminated patches + 128 never flagged), | |
| leaving 11,188. Rows whose only defect is a cosmetic template label | |
| were kept. Every kept turn was cleared by the judge. | |
| - Per tenet: min 141 / median 325 / max 529 scenarios. | |
| ## Running the interactive eval | |
| Needs the ConflictScope code (`andyjliu/conflictscope-dev`, branch `dev`, | |
| commit `6d2030a` or later) and an OpenAI-compatible endpoint serving the | |
| judge. Our runs use `Qwen/Qwen3.6-27B` as both judge and user simulator | |
| (`vllm serve Qwen/Qwen3.6-27B --tensor-parallel-size 8`); with the cache in | |
| place the user simulator is only called for ids missing from `cache.json`. | |
| The assistant under test is loaded in-process by `-m` (a HF id or local | |
| checkpoint; vLLM, one GPU for 7–8B models) or served remotely via | |
| `--assistant-api-base`. | |
| ```bash | |
| hf download value-generalization/conflictscope-eval-constitution-tenets-v3 --repo-type dataset --local-dir cs_v3 | |
| mkdir -p out && cp cs_v3/eval/cache.json out/ # eval writes CSVs next to the cache | |
| python conflictscope/src/evaluate_models.py \ | |
| -m <assistant model or checkpoint dir> \ | |
| -d cs_v3/eval/scenarios -o out --output-name <tag>.csv \ | |
| -i --cache --filter --temperature 0 \ | |
| --user-model Qwen/Qwen3.6-27B --user-api-base http://<judge-host>:8000/v1 \ | |
| --judge-model Qwen/Qwen3.6-27B --judge-api-base http://<judge-host>:8000/v1 \ | |
| [--steer-prompt prompt.txt] | |
| ``` | |
| `--temperature 0` selects the script's conversation defaults (assistant | |
| sampled at 1.0, 1,024 max tokens; judge at 0). Output CSV columns: | |
| `scenario_id, value1, value2, choice, likert, reasoning, conversation`. | |
| Steerability toward a tenet is then read off the `choice`/`likert` columns | |
| over the rows where that tenet is `value1` or `value2`, against a base | |
| model's CSV on the same scenarios. Copy the cache (don't symlink it into a | |
| shared dir) — the script rewrites it in place on a cache miss. | |
| ## Caveats | |
| - `check_results.user_prompt_clean` is `null` for rows that never passed the | |
| generation filters (no opening turn was ever written for them). | |
| - Opening turns were written by Qwen3.6-27B and reflect its style; a few | |
| kept turns carry a harmless bare `**Artifact**` label. | |
| - The base-eval CSVs for our reference models are not included. | |
| ## License | |
| Apache-2.0. Scenarios and opening turns are model-generated (Qwen3.6-27B); | |
| released for research use as part of the value-generalization project. | |