Datasets:

teilomillet
/

dmbench-examples

---
pretty_name: DMBench — French decision examples (v4)
language:
  - fr
license: mit
size_categories:
  - n<1K
tags:
  - dmbench
  - decision-making
  - evaluation
  - examples
  - synthetic
  - reranking
configs:
  - config_name: default
    data_files:
      - split: examples
        path: examples.jsonl
---
# DMBench examples

**90 structured examples: nine per category**, drawn from the rebuilt
`typed-decisions-fr-v4` development benchmark. Each of the ten categories has
three **Choice**, three **Score** and three **Noul** examples, spanning short,
medium and long prompts within that category and response type.

These are clear fictional French service and workplace scenarios. Each row
contains the complete model input, answer options or rubric, the reference answer,
a concise explanation, a native reference response and its evaluation.

- Read the [worked examples](EXAMPLES.md), or browse the `examples` split above.
- See [three Score reranking comparisons](RERANKING.md): laptop returns, live
  ticket availability and catering offers. Each uses a selected Score row and
  describes an alternative candidate under the same user demand and rubric.
- File hashes and the source identity are in [MANIFEST.json](MANIFEST.json).

**Reference responses are authored, gold-derived illustrations. No evaluated
model produced them.** Their one-hot probabilities explain the response format;
they are not measured confidence or evidence of model accuracy. Their passing
evaluations follow by construction. There are no model results or rankings here.

## What the categories mean

| Category | Practical decision |
| --- | --- |
| `conditions` | Check eligibility against explicit service requirements. |
| `action` | Choose a useful next step, respect prerequisites and avoid duplicate effects. |
| `quand-appeler` | Decide whether an external tool is needed, authorized and ready. |
| `securite` | Check identity, authority and operation scope; assess established incident status. |
| `routage` | Match a request to the responsible human service. |
| `preuves` | Assess support from relevant, current, competent and validated sources. |
| `contraintes` | Prioritize mandatory requirements over optional preferences. |
| `mise-a-jour` | Follow the current request after corrections, additions or cancellations. |
| `donnees` | Check verified recipients and permission for each disclosed field. |
| `escalade` | Choose a human handoff and its operational priority. |

The supplied rules define each exercise. They do not claim to describe universal
law or real incidents, and no external legal or medical knowledge is required.

## Load the data

```python
import json
from datasets import load_dataset
examples = load_dataset("teilomillet/dmbench-examples", split="examples")
row = examples[0]
# Send only this object to the model.
request = json.loads(row["request_json"])
print(json.dumps(request, ensure_ascii=False, indent=2))
# These fields belong to the evaluator.
print(row["expected_answer"])
print(row["explanation"])
print(json.loads(row["reference_response_json"]))
print(json.loads(row["evaluation_json"]))
```

The same 90 records are available in [examples.jsonl](examples.jsonl).
`state`, `instructions` and `options` are display columns. `request_json` preserves
the exact native input and option order: Choice/Noul criteria are mappings,
whereas Score criteria are ordered lists. JSON text columns keep those different
structures compatible with a single table schema.

IDs, length bands, reference answers, explanations and evaluation fields must
stay outside model features. The release IDs do not expose source endpoint
orientation, full source family IDs or an unreleased answer pattern.

## Choice, Score and Noul

The native wire format is `wyat-answer-v1`. The name identifies the response
contract; it does not attribute these reference responses to Wyat.

- **Choice** selects one supplied option and provides probabilities and a ranking.
  Accuracy checks the declared selection against the reference answer.
- **Score** rates an ordered rubric. It returns a distribution over levels and
  `expected_ordinal_value = sum(index * probability)`. This build uses three,
  four or five levels where their distinctions make sense. For example, 20%
  probability at level 2 and 80% at level 3 produces an expected index of 2.8.
  Modal-level accuracy, expected-index error, normalized MAE and ranked probability
  score are separate metrics. A high value can mean suitability, severity or
  urgency according to the stated axis; raw values across axes are not a common
  utility.
- **Noul** evaluates a direct true/false proposition with `probability_true`.
  The example evaluator uses `>= 0.5` to select true. Noul is not abstention;
  unknown facts must not silently become false facts.

Score can support **reranking**: hold the user demand, common context and rubric
fixed, score each candidate independently, then sort by the expected score in
that axis. The [worked comparisons](RERANKING.md) show reference rubric levels;
actual model expectations may be fractional. These are illustrative two-candidate
comparisons, not listwise retrieval measurements or calibrated cross-query
relevance. Documentary support does not by itself measure answer completeness.

Missing, invalid, unsupported and non-OK responses remain failures in evaluation
denominators. A `null` per-example metric means it does not apply to that response
type. This preview does not estimate calibration, robustness or general ability.

## Selection and release scope

There are exactly three rows per category/response-type panel. Prompt length is
the Unicode character count of the serialized native `request_json`.

For each panel, choose a short prompt and a long prompt from distinct scenario
families, then a middle prompt from a third family. Except for three curated
Score anchors, these are the shortest, longest and available prompt nearest their
length midpoint. The middle Score examples for `action`, `quand-appeler` and
`contraintes` retain the laptop-return, ticket-availability and catering cases
reviewed before this release; their families are reserved before selecting the
short and long examples. The underlying inputs are unchanged.

This is a length-stratified illustration with reviewed anchors, not a random or
statistically representative sample. Length ranges are modest because the
rebuilt scenarios are deliberately concise. The 90 structured rows use distinct
source families. The reranking page additionally describes three alternative
candidate summaries from the approved comparisons.

The full 1,200-question suite, complete scenario families, source authoring files,
generators, local audit archives and model results are outside this release.

## Checks and limits

The source candidate received label-blind LLM readings of all current inputs and
pairwise reviews of its scenarios. Ambiguities were corrected and changed inputs
were reread. These are authoring checks, not human certification or independent
statistical confirmation. The source still has limited scenario coverage and
residual surface cues; this small release is not a model-quality evaluation.

The packaged reference responses are parsed and graded with the native scorer.
The JSONL schema is checked through Hugging Face datasets/Arrow. Source, scorer
and published file hashes are recorded in the manifest. None of these mechanics
establishes practical model reliability or training-overlap freedom.

These examples are intentionally exposed. Keep them out of untouched confirmation
claims and disclose their use during model development. No model was rerun to
produce this release.

Original authored examples and documentation are distributed under the included
[MIT license](LICENSE). Project: [DMBench](https://github.com/teilomillet/dmbench).

	---
	pretty_name: DMBench — French decision examples (v4)
	language:
	- fr
	license: mit
	size_categories:
	- n<1K
	tags:
	- dmbench
	- decision-making
	- evaluation
	- examples
	- synthetic
	- reranking
	configs:
	- config_name: default
	data_files:
	- split: examples
	path: examples.jsonl
	---

	# DMBench examples

	90 structured examples: nine per category, drawn from the rebuilt
	`typed-decisions-fr-v4` development benchmark. Each of the ten categories has
	three Choice, three Score and three Noul examples, spanning short,
	medium and long prompts within that category and response type.

	These are clear fictional French service and workplace scenarios. Each row
	contains the complete model input, answer options or rubric, the reference answer,
	a concise explanation, a native reference response and its evaluation.

	- Read the [worked examples](EXAMPLES.md), or browse the `examples` split above.
	- See [three Score reranking comparisons](RERANKING.md): laptop returns, live
	ticket availability and catering offers. Each uses a selected Score row and
	describes an alternative candidate under the same user demand and rubric.
	- File hashes and the source identity are in [MANIFEST.json](MANIFEST.json).

	**Reference responses are authored, gold-derived illustrations. No evaluated
	model produced them.** Their one-hot probabilities explain the response format;
	they are not measured confidence or evidence of model accuracy. Their passing
	evaluations follow by construction. There are no model results or rankings here.

	## What the categories mean

	\| Category \| Practical decision \|
	\| --- \| --- \|
	\| `conditions` \| Check eligibility against explicit service requirements. \|
	\| `action` \| Choose a useful next step, respect prerequisites and avoid duplicate effects. \|
	\| `quand-appeler` \| Decide whether an external tool is needed, authorized and ready. \|
	\| `securite` \| Check identity, authority and operation scope; assess established incident status. \|
	\| `routage` \| Match a request to the responsible human service. \|
	\| `preuves` \| Assess support from relevant, current, competent and validated sources. \|
	\| `contraintes` \| Prioritize mandatory requirements over optional preferences. \|
	\| `mise-a-jour` \| Follow the current request after corrections, additions or cancellations. \|
	\| `donnees` \| Check verified recipients and permission for each disclosed field. \|
	\| `escalade` \| Choose a human handoff and its operational priority. \|

	The supplied rules define each exercise. They do not claim to describe universal
	law or real incidents, and no external legal or medical knowledge is required.

	## Load the data

	```python
	import json
	from datasets import load_dataset

	examples = load_dataset("teilomillet/dmbench-examples", split="examples")
	row = examples[0]

	# Send only this object to the model.
	request = json.loads(row["request_json"])
	print(json.dumps(request, ensure_ascii=False, indent=2))

	# These fields belong to the evaluator.
	print(row["expected_answer"])
	print(row["explanation"])
	print(json.loads(row["reference_response_json"]))
	print(json.loads(row["evaluation_json"]))
	```

	The same 90 records are available in [examples.jsonl](examples.jsonl).
	`state`, `instructions` and `options` are display columns. `request_json` preserves
	the exact native input and option order: Choice/Noul criteria are mappings,
	whereas Score criteria are ordered lists. JSON text columns keep those different
	structures compatible with a single table schema.

	IDs, length bands, reference answers, explanations and evaluation fields must
	stay outside model features. The release IDs do not expose source endpoint
	orientation, full source family IDs or an unreleased answer pattern.

	## Choice, Score and Noul

	The native wire format is `wyat-answer-v1`. The name identifies the response
	contract; it does not attribute these reference responses to Wyat.

	- Choice selects one supplied option and provides probabilities and a ranking.
	Accuracy checks the declared selection against the reference answer.
	- Score rates an ordered rubric. It returns a distribution over levels and
	`expected_ordinal_value = sum(index * probability)`. This build uses three,
	four or five levels where their distinctions make sense. For example, 20%
	probability at level 2 and 80% at level 3 produces an expected index of 2.8.
	Modal-level accuracy, expected-index error, normalized MAE and ranked probability
	score are separate metrics. A high value can mean suitability, severity or
	urgency according to the stated axis; raw values across axes are not a common
	utility.
	- Noul evaluates a direct true/false proposition with `probability_true`.
	The example evaluator uses `>= 0.5` to select true. Noul is not abstention;
	unknown facts must not silently become false facts.

	Score can support reranking: hold the user demand, common context and rubric
	fixed, score each candidate independently, then sort by the expected score in
	that axis. The [worked comparisons](RERANKING.md) show reference rubric levels;
	actual model expectations may be fractional. These are illustrative two-candidate
	comparisons, not listwise retrieval measurements or calibrated cross-query
	relevance. Documentary support does not by itself measure answer completeness.

	Missing, invalid, unsupported and non-OK responses remain failures in evaluation
	denominators. A `null` per-example metric means it does not apply to that response
	type. This preview does not estimate calibration, robustness or general ability.

	## Selection and release scope

	There are exactly three rows per category/response-type panel. Prompt length is
	the Unicode character count of the serialized native `request_json`.

	For each panel, choose a short prompt and a long prompt from distinct scenario
	families, then a middle prompt from a third family. Except for three curated
	Score anchors, these are the shortest, longest and available prompt nearest their
	length midpoint. The middle Score examples for `action`, `quand-appeler` and
	`contraintes` retain the laptop-return, ticket-availability and catering cases
	reviewed before this release; their families are reserved before selecting the
	short and long examples. The underlying inputs are unchanged.

	This is a length-stratified illustration with reviewed anchors, not a random or
	statistically representative sample. Length ranges are modest because the
	rebuilt scenarios are deliberately concise. The 90 structured rows use distinct
	source families. The reranking page additionally describes three alternative
	candidate summaries from the approved comparisons.

	The full 1,200-question suite, complete scenario families, source authoring files,
	generators, local audit archives and model results are outside this release.

	## Checks and limits

	The source candidate received label-blind LLM readings of all current inputs and
	pairwise reviews of its scenarios. Ambiguities were corrected and changed inputs
	were reread. These are authoring checks, not human certification or independent
	statistical confirmation. The source still has limited scenario coverage and
	residual surface cues; this small release is not a model-quality evaluation.

	The packaged reference responses are parsed and graded with the native scorer.
	The JSONL schema is checked through Hugging Face datasets/Arrow. Source, scorer
	and published file hashes are recorded in the manifest. None of these mechanics
	establishes practical model reliability or training-overlap freedom.

	These examples are intentionally exposed. Keep them out of untouched confirmation
	claims and disclose their use during model development. No model was rerun to
	produce this release.

	Original authored examples and documentation are distributed under the included
	[MIT license](LICENSE). Project: [DMBench](https://github.com/teilomillet/dmbench).