ManhHoDinh/titlegen-benchmark-luna

TitleGen: Multilingual Title Generation Evaluated by Luna

Tiếng Việt: cách dùng model, đo benchmark và đọc feedback

Evaluate your own model

Download benchmark.json, titlegen_benchmark.py and api_generate.py from this dataset. Python's standard library is enough to prepare inputs, validate your predictions and produce an unscored structural report; no account or provider calls are required. Follow QUICKSTART.md.

File	Purpose
benchmark.json	Original inputs, split labels, applicable criteria and rubric
titlegen_benchmark.py	Prepare, validate and optionally judge your predictions
api_generate.py	Generate titles with your explicitly selected model endpoint
custom_generate.py	Adapter skeleton for local model inference
benchmark-source.zip	Source, tests, scoped code license and reproducibility manifest
RELEASE.md	Verified release scope, status interpretation and optimization workflow
END_TO_END.md	Download a pinned release, run a Hub model locally and export your benchmark report
local_generate.py	Ready-to-run CPU Transformers generation with a pinned model revision

python3 titlegen_benchmark.py prepare --benchmark benchmark.json --partition evaluation --output inputs.jsonl
# Optional: call your explicitly selected model endpoint to create predictions.
python3 api_generate.py --inputs inputs.jsonl --output predictions.jsonl --model my-title-model --base-url "$TITLEGEN_MODEL_URL"
python3 titlegen_benchmark.py validate --benchmark benchmark.json --predictions predictions.jsonl --partition evaluation
python3 titlegen_benchmark.py evaluate --benchmark benchmark.json --predictions predictions.jsonl --model-name my-title-model --output my-report

Optionally configure your own compatible judge endpoint explicitly to obtain scores and reasons. The portable protocol is distinct from the historical Luna primary protocol: record the exact benchmark hash, judge and generation settings and do not silently merge scores from different protocols.

Abstract

This benchmark reports four TitleGen model outputs on the same 3,400 inputs across 17 languages, with Luna scores, criterion-level reasons, supplemental feedback, and paired comparisons where available. This is an automated diagnostic snapshot. Production readiness is NOT_ESTABLISHED; pending records remain visible and are not counted as passes or assigned zero scores.

Interactive report | Dataset and row downloads

Task and data

Produce a short useful title for the original conversation. Each language has 200 inputs: 150 evaluation and 50 development (2,550 and 850 overall). Neither split is a training set. Tuning on evaluation inputs invalidates subsequent held-out claims for those inputs. The original messages, all model titles and available Luna reasons/advice are public with the owner's explicit authorization. Mixed-v2 combines external-source and authored examples; row metadata records source provenance. Pending annotations are not human gold. Source-specific terms continue to apply; no blanket license for third-party data is asserted here.

Models and generation

• sft
• dpo
• curriculum
• multilingual

Available generation settings and completion evidence are recorded in report.json / diagnostics.json. Pinned revisions are not asserted unless they are explicitly present in that snapshot. Local generations use each model's chat template, greedy decoding, 32 maximum new tokens and float32 on MPS. A token limit warning does not prove truncation. Luna baseline generation, when present, uses a separate task instruction and 128 maximum output tokens on the same inputs. Baseline completion is reported explicitly; missing titles remain PENDING. Local timing is descriptive and is not a production load test.

Evaluation protocol

The primary pointwise judge is ChatGPT 5.6 Luna via 9Router. Scores run from 0 to 4; per-criterion reasons are retained. Supplemental anonymized candidate feedback and proposed improved titles are separate from primary judgments. Advice is a hypothesis for experiments on dataset coverage, label quality, data generation, training or decoding, not a demonstrated causal diagnosis. Paired comparisons use common completed items; read the displayed sample counts and partitions. Supplemental and primary scores must not be pooled as interchangeable evidence.

Error analysis and feedback

Primary evaluation results

Model	Criterion	Mean / 4	Applicable evaluation items
curriculum	concision	3.511	2550
curriculum	entity_fidelity	2.074	2229
curriculum	faithfulness	3.021	2550
curriculum	fluency	2.694	2550
curriculum	intent_coverage	1.404	2505
curriculum	relevance	2.524	2550
curriculum	safety	3.928	318
dpo	concision	3.573	2550
dpo	entity_fidelity	2.105	2229
dpo	faithfulness	3.018	2550
dpo	fluency	2.676	2550
dpo	intent_coverage	1.478	2505
dpo	relevance	2.591	2550
dpo	safety	3.896	318
multilingual	concision	3.245	2550
multilingual	entity_fidelity	2.056	2229
multilingual	faithfulness	2.611	2550
multilingual	fluency	2.573	2550
multilingual	intent_coverage	1.249	2505
multilingual	relevance	2.467	2550
multilingual	safety	3.912	318
sft	concision	3.551	2550
sft	entity_fidelity	2.064	2229
sft	faithfulness	2.976	2550
sft	fluency	2.694	2550
sft	intent_coverage	1.455	2505
sft	relevance	2.547	2550
sft	safety	3.868	318

Counts exclude inapplicable or pending criteria; means are ordinal descriptive summaries. This table uses only evaluation rows, never development rows.

Supplemental coverage

Stage	Verified items	Expected items	Status
Supplemental feedback	3400	3400	FEEDBACK_COMPLETE
Luna baseline	3400	3400	FEEDBACK_COMPLETE

Feedback provenance: 3,227 inputs use the original five-candidate response protocol; 173 use versioned single-candidate response remediation with the same anonymous five-candidate context. The remediation subset consists of previously failed inputs and is selection-biased. Its paired scores are reported separately, never pooled with the original protocol. Per-row feedback_protocol and feedback_summary.remediation_sources retain the source policy and prior unknown-response counts; old failures are not erased.

Exact pointwise rubric anchors

Criterion	0	1	2	3	4
relevance	Unrelated to the request topic.	Mostly unrelated; only incidental topic overlap.	Recognizable topic but substantially misdirected.	On topic with a minor mismatch.	Precisely represents the request topic.
intent_coverage	Misses the central intent.	Captures a peripheral intent only.	Captures the main intent but omits a major qualifier or second intent.	Captures the main intent and nearly all essential qualifiers.	Covers all title-relevant key intents and qualifiers.
faithfulness	Fabricates or reverses the central meaning.	Adds a major unsupported claim.	Adds unsupported specificity or resolves important ambiguity without evidence.	Faithful except for a minor unsupported implication.	Every claim is supported; uncertainty and ambiguity are preserved.
entity_fidelity	Replaces or corrupts a central entity or critical literal.	Major entity, number, code, or relationship error.	Some essential entities or literals are missing or imprecise.	Essential entities preserved with a minor noncritical defect.	All necessary entities and critical literals retained accurately.
concision	Not a usable title; excessive answer text or repetition.	Severely verbose, repetitive, or padded.	Noticeably wordy or includes unnecessary detail.	Compact with only minor removable wording.	Economical, informative title with no unnecessary wording.
fluency	Unintelligible or wholly inappropriate for the target language.	Major grammar or target language errors impede understanding.	Understandable but awkward or partly inappropriate for the target language.	Natural target language with a minor grammatical or idiomatic defect.	Clear, idiomatic, grammatically sound, and appropriate for the target language.
safety	Directly facilitates serious harm or exposes sensitive personal data.	Substantially endorses harm or reveals unnecessary sensitive details.	Contains a meaningful unsafe implication, stigmatizing framing, or privacy lapse.	Safe overall with a minor avoidable framing concern.	Safely describes the request without harmful endorsement or unnecessary exposure.

Recorded status definitions

Status	Meaning
COMPLETE	All 3,400 model judgments passed stored response and final receipt readback.
FAILED	Completed evidence failed validation; this is an evidence failure, not a score.
FEEDBACK_COMPLETE	Raw baseline and feedback responses verified and joined to the immutable input row.
GENERATED	Some verified Luna baseline titles available; see generated_count and expected_count for coverage.
JUDGING	Incomplete shard has attempt artifacts or is locked; progress is not read while active.
MAJOR_DEFECT	Applicable criterion score 1/4; consult that criterion's rubric anchors. Internal categories derived from this rubric, not a universal industry standard or a production pass. Overall category uses the minimum applicable criterion score. Missing assessment is never zero.
MINOR_ISSUE	Applicable criterion score 3/4; consult that criterion's rubric anchors. Internal categories derived from this rubric, not a universal industry standard or a production pass. Overall category uses the minimum applicable criterion score. Missing assessment is never zero.
NEEDS_REVISION	Applicable criterion score 2/4; consult that criterion's rubric anchors. Internal categories derived from this rubric, not a universal industry standard or a production pass. Overall category uses the minimum applicable criterion score. Missing assessment is never zero.
NOT_ASSESSED	No applicable verified scores available; never interpreted as zero.
NOT_ESTABLISHED	Production readiness is not established by this benchmark.
PENDING	No completed verified judgment available; absence is not a failure.
PENDING_NOT_GOLD	Assessment expectations are pending agent annotations, not native-speaker gold.
STRONG	Applicable criterion score 4/4; consult that criterion's rubric anchors. Internal categories derived from this rubric, not a universal industry standard or a production pass. Overall category uses the minimum applicable criterion score. Missing assessment is never zero.
UNUSABLE	Applicable criterion score 0/4; consult that criterion's rubric anchors. Internal categories derived from this rubric, not a universal industry standard or a production pass. Overall category uses the minimum applicable criterion score. Missing assessment is never zero.

Use the report filters to inspect language, partition, model, criteria and status. Each original input has its model outputs and recorded score/reason/feedback state. status_meanings, weaknesses, suggestions, and feedback_summary explain the available categories. COMPLETE means that stage completed, not production approval. PENDING, incomplete feedback, failed validation and missing evidence remain visible.

Production gates and limitations

This is a versioned research/software release, not a peer-reviewed paper or production certification. No single composite leaderboard score is defined: compare the seven criteria with their applicable denominators and inspect paired results within each protocol. Small differences in ordinal means do not establish statistical significance or a practically meaningful winner.

Read production_readiness for measured, failed and missing gates. Full automated coverage alone cannot establish production readiness: independent native review, safety and adversarial evidence, data leakage checks, latency/load and deployment checks require their own evidence. The current 200 items/language do not meet a 500-items/language requirement. Luna judging its own baseline creates self-bias; no independent Luna superiority, human agreement, SOTA or causal improvement is claimed. A suggested replacement title is not human gold.

Reproducibility and current snapshot

• Snapshot timestamp: 2026-09-08T12:34:30.485685+00:00
• Protocol: titlegen-mixed-v2-luna-private-diagnostics-v1
• Canonical public snapshot SHA-256: 0bad09e9eb55b115fbff00d30b2dfdf6e651ab739ee9dc64bbd2630329b0589f
• Every artifact hash is in artifact-manifest.json; remote bytes are verified after each atomic commit. Refreshes create new Git revisions.
• diagnostics.json retains all 3,400 rows; report.json contains aggregates.
• Generation identities and completion counts remain in the machine-readable report.
• Download benchmark-source.zip from the dataset: project-owned benchmark code, import dependency closure, focused tests, rubric and production policy. Extract it and follow REPRODUCE.md; SOURCE-MANIFEST.json records source revision, exact source hashes and external import inventory. Private run receipts and provider credentials must be supplied separately for new paid judging runs.

Citation

@misc{titlegen_luna_benchmark_2026,
  author = {Manh Ho Dinh},
  title = {TitleGen: Multilingual Title Generation Evaluated by Luna},
  year = {2026},
  howpublished = {Hugging Face dataset and interactive benchmark},
  url = {https://huggingface.co/datasets/ManhHoDinh/titlegen-benchmark-luna},
  note = {Snapshot 0bad09e9eb55b115fbff00d30b2dfdf6e651ab739ee9dc64bbd2630329b0589f}
}

Licensing and release scope

Benchmark code is a separate release from model weights and source datasets. The TitleGen model cards identify the upstream Liquid AI LFM1.0 terms: upstream model license. Publishing this benchmark does not relicense those weights. Code bundle licensing applies only to files explicitly included in that bundle. Consult source provenance and terms before redistributing third-party data; unresolved licensing metadata is not replaced by an invented blanket Apache license.