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ARXIV:2603.01353 · DOMAIN-SPECIFIC LLMS · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE
ARXIV:2603.01353DOMAIN-SPECIFIC LLMSSUBMITTED 19 MAR · 21:31 UTCFRESHNESS STALEarXiv
Build domain-specific datasets for improving reasoning in LLMs with demonstrated success in the Japanese financial sector.
Opportunity summary
Pain Build domain-specific datasets for improving reasoning in LLMs with demonstrated success in the Japanese financial sector.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Build domain-specific datasets for improving reasoning in LLMs with demonstrated success in the Japanese financial sector. This study proposes a general method for constructing high-quality synthetic instruction data for any domain, starting from domain-specific…
In adapting LLMs to specific domains, achieving both domain expertise and reasoning ability remains an urgent challenge. This study proposes a general method for constructing high-quality synthetic instruction data for any domain, starting from…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Evaluation results confirmed performance improvements over baseline models on financial benchmarks, demonstrating the effectiveness of our approach.
Domain-Specific LLMs moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Build domain-specific datasets for improving reasoning in LLMs with demonstrated success in the Japanese financial sector.
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Paper Pack
10.48550/arXiv.2603.01353Build domain-specific datasets for improving reasoning in LLMs with demonstrated success in the Japanese financial sector.
Abstract
In adapting LLMs to specific domains, achieving both domain expertise and reasoning ability remains an urgent challenge. This study proposes a general method for constructing high-quality synthetic instruction data for any domain, starting from domain-specific vocabulary. As a demonstration, we applied this method to the financial domain and constructed a large-scale instruction dataset totaling approximately 9.5 billion tokens with Chain-of-Thought reasoning traces. Evaluation results confirmed performance improvements over baseline models on financial benchmarks, demonstrating the effectiveness of our approach. We also report findings on the impact of reasoning trace length on performance and its limitations. Lastly, we open-source our models and datasets on https://huggingface.co/nri-ai .
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
Build domain-specific datasets for improving reasoning in LLMs with demonstrated success in the Japanese financial sector. This study proposes a general method for constructing high-quality synthetic instruction data for any domain, starting from domain-specific vocabulary.
METHOD
In adapting LLMs to specific domains, achieving both domain expertise and reasoning ability remains an urgent challenge. This study proposes a general method for constructing high-quality synthetic instruction data for any domain, starting from domain-specific vocabulary.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Evaluation results confirmed performance improvements over baseline models on financial benchmarks, demonstrating the effectiveness of our approach.
WHY NOW
Domain-Specific LLMs moved forward this cycle; last verified April 2026. Public score 8.0/10.
This study proposes a general method for constructing high-quality synthetic instruction data for any domain, starting from domain-specific vocabulary.
This is a core methodological contribution explicitly stated in the abstract.
partial
As a demonstration, we applied this method to the financial domain and constructed a large-scale instruction dataset totaling approximately 9.5 billion tokens with Chain-of-Thought reasoning traces.
This is a direct application of the proposed method as described in the abstract.
partial
constructed a large-scale instruction dataset totaling approximately 9.5 billion tokens with Chain-of-Thought reasoning traces.
A specific quantitative detail about the dataset size is provided in the abstract.
partial
with Chain-of-Thought reasoning traces.
This is a specific characteristic of the dataset mentioned in the abstract.
partial
Evaluation results confirmed performance improvements over baseline models on financial benchmarks, demonstrating the effectiveness of our approach.
This is a key outcome and result of the study, explicitly stated in the abstract.
partial
We also report findings on the impact of reasoning trace length on performance and its limitations.
This is a specific finding that the paper claims to report.
partial
and its limitations.
This is a specific limitation that the paper claims to report.
partial
Lastly, we open-source our models and datasets on https://huggingface.co/nri-ai .
This is a direct statement about the availability of resources, including a URL.
partial
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Concepts
Methods
Materials
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Competitors
Build domain-specific datasets for improving reasoning in LLMs with demonstrated success in the Japanese financial sector.
Segment
Domain-Specific LLMs
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
Adjacent
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CITED BY
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 33% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 33% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
Buzz trend pending.