Update README.md

README.md

@@ -18,13 +18,13 @@ configs:
 
 - config_name: intent_v1
   data_dir: intent_v1
-  
+
 - config_name: rag_verification_v1
   data_dir: rag_verification_v1
-  
+
 - config_name: minimal_pairs_v1
   data_dir: minimal_pairs_v1
-    
+
 - config_name: az_tr_interference_v1
   data_dir: az_tr_interference_v1
 
@@ -33,4 +33,235 @@ configs:
 
 - config_name: rag_selection_v1
   data_dir: rag_selection_v1
----
+
+language:
+- az
+license: cc-by-4.0
+size_categories:
+- 1K<n<10K
+task_categories:
+- question-answering
+- text-classification
+- multiple-choice
+- text-retrieval
+tags:
+- azerbaijani
+- benchmark
+- evaluation
+- rag
+- hallucination
+pretty_name: YOXLA Benchmark
+---
+
+# YOXLA Benchmark
+
+1443 frozen examples for evaluating large language models in
+Azerbaijani, across four blocks and eleven tasks.
+
+Run with the [YOXLA framework](https://github.com/LocalDoc-Azerbaijan/yoxla):
+
+```bash
+pip install "yoxla[api]"
+yoxla run --provider openrouter --model <model> --block all
+```
+
+Or load a config directly:
+
+```python
+from datasets import load_dataset
+
+data = load_dataset("LocalDoc/YOXLA-Benchmark", "rag_selection_v1")["test"]
+```
+
+## What makes it different
+
+**Every answer space is closed.** A label, a number, or a span quoted
+from a passage the model was given. Nothing is scored by a judge
+model, nothing depends on deciding whether two spellings of a name
+mean the same thing, and every score can be reproduced from stored
+output.
+
+That decision cost the benchmark two tasks. An orthography block that
+scored whether a model *writes* Azerbaijani correctly, and an
+open-answer knowledge task, were both built and both dropped: sixty-two
+of the knowledge set's hundred and fifty answers were multi-word
+entities — honorific titles no two people would word alike, names with
+up to seven equally correct forms — and every scoring dispute found in
+review came from that group.
+
+**No model decides a label.** Generators write passages, sentences and
+candidate options; the gold comes from Wikidata, from a named
+grammatical rule, from a dictionary, or from the construction of the
+item itself. A generation that does not match its source is rejected
+rather than relabelled.
+
+**The distractors are measured, not assumed.** Every set with a closed
+answer space is run past strategies that read nothing — pick the most
+famous option, pick the passage sharing the most words with the
+question, always answer the first one — and the build fails if any of
+them beats its floor. Where a cue cannot be removed, the floor it
+leaves is computed from the finished data and published with the task
+rather than hidden.
+
+## Blocks
+
+### Understanding — 600
+
+| Config | Rows | Inputs | Gold | Answer |
+| --- | --- | --- | --- | --- |
+| `extractive_qa_v1` | 200 | `context`, `question` | `answer` | a span quoted from the context |
+| `qa_abstention_v1` | 100 | `context`, `question` | `answer` | a span, or `Cavab yoxdur` |
+| `nli_v1` | 100 | `premise`, `hypothesis` | `label` | entailment / neutral / contradiction |
+| `sts_v1` | 100 | `sentence1`, `sentence2` | `score` | 0.0–5.0 |
+| `sentiment_v1` | 50 | `text` | `label` | positive / neutral / negative |
+| `intent_v1` | 50 | `text` | `label` | one of 25 banking intents |
+
+The two span tasks are scored on character offsets rather than on
+strings: the quote is located in the passage and its range compared
+with the gold's. The gold answer occurs exactly once in its own
+context in all 250 answerable examples, which is what makes the
+position unambiguous.
+
+### Language — 400
+
+| Config | Rows | Inputs | Gold | Answer |
+| --- | --- | --- | --- | --- |
+| `minimal_pairs_v1` | 200 | `sentence_a`, `sentence_b` | `label` | A / B |
+| `az_tr_interference_v1` | 200 | `sentence_a`, `sentence_b` | `label` | A / B |
+
+Both show two sentences differing in one word and ask which one is
+Azerbaijani. Position is balanced inside every breakdown, so answering
+"A" throughout scores 50.
+
+`minimal_pairs_v1` corrupts a named rule — vowel harmony, the question
+particle, the definite accusative, case government and four more — so
+the label follows from the rule rather than from an opinion.
+
+`az_tr_interference_v1` replaces an Azerbaijani word with a Turkish
+one:
+
+```text
+A) Uşaqlar məktəbdə çox gözəl danışmaq öyrənirlər.
+B) Uşaqlar məktəbdə çox gözəl konuşmak öyrənirlər.
+```
+
+Three lookups in two independent sources decide every pair: the
+Azerbaijani word is in a hunspell dictionary and occurs at least 200
+times in Azerbaijani Wikipedia, the Turkish word is in a Turkish
+dictionary and absent from the Azerbaijani one, and the Azerbaijani
+form outnumbers the Turkish one at least 50:1 in the corpus. Read
+`interference_type` beside the score: a `cognate` pair (`kitab` /
+`kitap`) asks which spelling Azerbaijani uses, a `distinct_lexeme`
+pair (`danışmaq` / `konuşmak`) asks which word it uses at all, and
+only the second is beyond a model that merely spells correctly.
+
+### Knowledge — 150
+
+| Config | Rows | Inputs | Gold | Answer |
+| --- | --- | --- | --- | --- |
+| `knowledge_choice_v1` | 150 | `question`, `candidates` | `answer_index` | 1–20 |
+
+Facts about Azerbaijan harvested from Wikidata across seven
+categories. A fact Wikidata answers more than one way is dropped at
+build time.
+
+Three cues are closed at build time so that recognition is not free:
+distractors come from the same relation, so the wrong *kind* of thing
+cannot be eliminated; the answer is not the most-linked option, so
+"pick the famous one" fails; a year distractor sits within a decade of
+the answer. Read `modal_answer_share` beside the score — a model that
+does not know tends to return the same number every time, and accuracy
+alone does not show it.
+
+### RAG — 293
+
+| Config | Rows | Inputs | Gold | Answer |
+| --- | --- | --- | --- | --- |
+| `rag_verification_v1` | 160 | `context`, `claim` | `label` | TƏSDİQ / ZİDD / YOXDUR |
+| `rag_selection_v1` | 133 | `question`, `passages` | `label` | 1–6, or HEÇ BİRİ |
+
+The two halves of a retrieval system, measured apart: selection asks
+whether the right passage was picked up, verification asks what the
+model does with a passage once it has one.
+
+`rag_verification_v1` gives a passage and one claim. Its four claim
+types are split out by `claim_type`, and the one worth reading is
+`counterfactual`: the passage states an altered value — a year moved,
+a district changed — and the claim states the real one. A model
+answering `TƏSDİQ` has read its own memory instead of the text, which
+is the failure that makes a retrieval system quietly wrong. Absence is
+decidable here because each passage is written from a known list of
+three facts, so anything outside that list is provably not stated.
+
+`rag_selection_v1` gives a question and six short passages, about
+seventy words in all. The negatives are the measurement: passages
+about the same subject but a different aspect, passages about the same
+aspect but a different subject, and passages whose value is of the
+same kind as the answer. One item in seven has its answer in none of
+them — `per_item_type_accuracy` splits those out, and a system that
+always returns its best guess fails there and nowhere else.
+
+**One caveat specific to this task.** The word-overlap cue cannot be
+driven to chance: the gold is the only passage carrying both the
+subject and the relation, and a negative carrying both would be a
+passage stating the answer. The gold is held to a *tie* with the other
+passages about its subject instead, which leaves a floor near 0.35 for
+"pick the passage sharing the most words" — against a chance of 0.14
+and against floors at chance everywhere else in this benchmark. Read
+the score with that in mind.
+
+## Sources and how the gold was made
+
+| Source | Used for | Licence |
+| --- | --- | --- |
+| [Wikidata](https://www.wikidata.org) | the facts behind knowledge, RAG verification and RAG selection | CC0 |
+| Azerbaijani Wikipedia (`20231101.az`) | passages for the QA blocks; word frequencies for the interference set | CC BY-SA 4.0 |
+| [MozillaAZ spellchecker](https://github.com/mozillaz/spellchecker) | whether a form is a word of Azerbaijani | see repository |
+| [LibreOffice `tr_TR`](https://github.com/LibreOffice/dictionaries) | whether a form is a word of Turkish | see repository |
+
+The dictionaries and the frequency table were used as lookups during
+construction and are not redistributed here. Each is trusted in one
+direction only: presence in the dictionary proves a form is
+Azerbaijani, and absence proves nothing, because its verb paradigms
+have holes.
+
+Generator models wrote passages, sentences and questions. They never
+decided a label, and no model output was accepted without passing the
+deterministic check for its task.
+
+## What this benchmark does not measure
+
+**Production.** No task asks a model to write Azerbaijani and scores
+what it wrote. The same model recognised correct grammar at 90.5 and,
+in the same run, scored 57.7 on writing it — the two are not
+interchangeable, and only the first number exists here.
+
+**Recall.** Knowledge asks a model to pick a fact out of twenty
+candidates, never to produce it. The interference task asks it to
+recognise a Turkish word, not to avoid writing one.
+
+**Anything above sentence level.** No discourse, no long context, no
+multi-turn behaviour.
+
+Scoring these would need a judge model or human annotation. A judge
+would put one model's Azerbaijani inside the loop that measures
+Azerbaijani, and would end reproducibility.
+
+## Versioning
+
+A task id is frozen from its first publication. A substantial change
+means a new id — `nli_v2` — never an edit in place, and the superseded
+task stays runnable so an older run can be reproduced. Pin a revision
+for a comparable run:
+
+```bash
+yoxla run --provider openai --model <model> --block all --revision <commit>
+```
+
+The framework bundles a manifest of row counts and content
+fingerprints per block and warns when the data here has changed since
+a run was scored.
+
+## Licence
+
+Data: CC BY 4.0. Framework: Apache-2.0.