Korean-Specialized Modeling
Korean specialization is validated across tokenization, retrieval, long instructions, and work artifacts—not translated labels.
Address morphology, endings, spacing, token efficiency, Korean-English mixing, long sessions, and evaluation gaps together.
§ 01
The operating conditions that justify Korean-Specialized Modeling
Language differences may appear small in a short query while instruction and terminology errors accumulate across a long session.
English-centered coding and reasoning benchmarks do not directly measure Korean instructions or Korean organizational documents.
- Korean instructions must persist through multiple execution and review stages
- Mixed Korean-English documents, code, and organizational terms are core context
- Only interface translation is required
- Korean held-out tasks and human evaluators cannot be prepared
Korean-Specialized Modeling: system plate
| Option | Fit | Evidence | Cost |
|---|---|---|---|
| Apply capability | Korean instructions must persist through multiple execution and review stages | Token efficiency and context length | Ablate retrieval, tokenizer, prompting, and training to locate the source of error. |
| Alternative | Only interface translation is required | Long-session instruction retention | No superiority claim is based on unpublished comparison scores. |
- The workflow begins with Diagnose token length, segmentation, morphology, and code mixing on real input samples..
- It reaches an acceptance decision through Token efficiency and context length.
§ 03
Fix the boundary and acceptance criteria before implementation.
Korean specialization is validated across tokenization, retrieval, long instructions, and work artifacts—not translated labels.
- 01
Stage 1
Diagnose token length, segmentation, morphology, and code mixing on real input samples.
Review artifact 1 - 02
Stage 2
Ablate retrieval, tokenizer, prompting, and training to locate the source of error.
Review artifact 2 - 03
Stage 3
Evaluate preservation of terminology, authority, and requirements across long Korean sessions.
Review artifact 3 - 04
Stage 4
Record Korean evaluator agreement and work-artifact review alongside automated scores.
Review artifact 4
§ 04
Hypothetical workloads make the applicability boundary concrete.
Korean instructions must persist through multiple execution and review stages
Language differences may appear small in a short query while instruction and terminology errors accumulate across a long session.
- APPROACH
- Diagnose token length, segmentation, morphology, and code mixing on real input samples.
- BOUNDARY
- No superiority claim is based on unpublished comparison scores.
Mixed Korean-English documents, code, and organizational terms are core context
English-centered coding and reasoning benchmarks do not directly measure Korean instructions or Korean organizational documents.
- APPROACH
- Ablate retrieval, tokenizer, prompting, and training to locate the source of error.
- BOUNDARY
- Language effects from one task are not generalized to every model and workload.
§ 05
Review gains and costs in the same table.
| Decision | Gain | Cost | Watch |
|---|---|---|---|
| Korean instructions must persist through multiple execution and review stages | Diagnose token length, segmentation, morphology, and code mixing on real input samples. | No superiority claim is based on unpublished comparison scores. | Token efficiency and context length |
| Mixed Korean-English documents, code, and organizational terms are core context | Ablate retrieval, tokenizer, prompting, and training to locate the source of error. | Language effects from one task are not generalized to every model and workload. | Long-session instruction retention |
Korean-Specialized Modeling: system plate
Is Korean-Specialized Modeling the next justified intervention?
- Korean instructions must persist through multiple execution and review stagesProceed to design
Diagnose token length, segmentation, morphology, and code mixing on real input samples.
- Only interface translation is requiredUse the alternative path
No superiority claim is based on unpublished comparison scores.
- The workflow begins with Diagnose token length, segmentation, morphology, and code mixing on real input samples..
- It reaches an acceptance decision through Token efficiency and context length.
§ 07
Agree on measurement conditions before publishing a result.
| Measure | Method | Pass condition | Caveat |
|---|---|---|---|
| Token efficiency and context length | Diagnose token length, segmentation, morphology, and code mixing on real input samples. | Repeated runs satisfy the acceptance threshold agreed during discovery | No superiority claim is based on unpublished comparison scores. |
| Long-session instruction retention | Ablate retrieval, tokenizer, prompting, and training to locate the source of error. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
| Korean evaluator agreement | Evaluate preservation of terminology, authority, and requirements across long Korean sessions. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
§ 08
Conditions for not applying the capability are part of the design.
Only interface translation is required
No superiority claim is based on unpublished comparison scores.
Korean held-out tasks and human evaluators cannot be prepared
Language effects from one task are not generalized to every model and workload.
§ 09
Proceed through diagnosis, design, and validation gates.
- 01
Diagnosis
PattyAnalyze the current system and its failure signals.
ClientProvide representative work, data boundaries, and operating constraints.
Token efficiency and context length - 02
Design
PattyAblate retrieval, tokenizer, prompting, and training to locate the source of error.
ClientConfirm owners and acceptance criteria.
Long-session instruction retention - 03
Validation
PattyEvaluate preservation of terminology, authority, and requirements across long Korean sessions.
ClientMake the production-transition or stop decision.
Korean evaluator agreement
§ 10
Artifacts remain with the operating organization after the engagement.
- Korean-Specialized Modeling decision record
- Korean specialization is validated across tokenization, retrieval, long instructions, and work artifacts—not translated labels.Client-owned · Patty-reviewed
- Validation harness and acceptance criteria
- Token efficiency and context length · Long-session instruction retention · Korean evaluator agreementJointly maintained
- Operations and recovery runbook
- No superiority claim is based on unpublished comparison scores. · Language effects from one task are not generalized to every model and workload.Operating-team owned
§ 11
Use shared terms with explicit operating meaning.
- Korean-Specialized Modeling
- Address morphology, endings, spacing, token efficiency, Korean-English mixing, long sessions, and evaluation gaps together.
- Acceptance criterion
- Token efficiency and context length
- Operating boundary
- No superiority claim is based on unpublished comparison scores.
REFERENCES
References and primary material
- HumanEval-XL
Primary material for the method and terminology.
- Multilingual prompt code-generation research
Primary material for the method and terminology.
Begin by determining whether Korean-Specialized Modeling is the justified next step.
We define scope and validation against representative work, data and infrastructure boundaries, and explicit failure conditions.