Constructing Synthetic Instruction Datasets for Improving Reasoning in Domain-Specific LLMs: A Case Study in the Japanese Financial Domain

Constructing Synthetic Instruction Datasets for Improving Reasoning in Domain-Specific LLMs: A Case Study in the Japanese Financial Domain | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/constructing-synthetic-instruction-datasets-for-improving-reasoning-in-domain-specific-llms-a-case-study-in-the-japanese

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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Canonical ID constructing-synthetic-instruction-datasets-for-improving-reasoning-in-domain-specific-llms-a-case-study-in-the-japanese | Route /signal-canvas/constructing-synthetic-instruction-datasets-for-improving-reasoning-in-domain-specific-llms-a-case-study-in-the-japanese

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/constructing-synthetic-instruction-datasets-for-improving-reasoning-in-domain-specific-llms-a-case-study-in-the-japanese

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Constructing Synthetic Instruction Datasets for Improving Reasoning in Domain-Specific LLMs: A Case Study in the Japanese Financial Domain

PDF: https://arxiv.org/pdf/2603.01353v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-19T21:31:49.672Z

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Watch and verify: Constructing Synthetic Instruction Datasets for Improving Reasoning in Domain-Specific LLMs: A Case Study in the Japanese Financial Domain

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Subject: Constructing Synthetic Instruction Datasets for Improving Reasoning in Domain-Specific LLMs: A Case Study in the Japanese Financial Domain

Verdict

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
This study proposes a general method for constructing high-quality synthetic instruction data for any domain, starting from domain-specific vocabulary.
Implicationpartial
This is a core methodological contribution explicitly stated in the abstract.
Verificationpartial
partial
Evidencepartial
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.
Implicationpartial
This is a direct application of the proposed method as described in the abstract.
Verificationpartial
partial
Evidencepartial
constructed a large-scale instruction dataset totaling approximately 9.5 billion tokens with Chain-of-Thought reasoning traces.
Implicationpartial
A specific quantitative detail about the dataset size is provided in the abstract.
Verificationpartial
partial
Evidencepartial
with Chain-of-Thought reasoning traces.
Implicationpartial
This is a specific characteristic of the dataset mentioned in the abstract.
Verificationpartial
partial
Evidencepartial
Evaluation results confirmed performance improvements over baseline models on financial benchmarks, demonstrating the effectiveness of our approach.
Implicationpartial
This is a key outcome and result of the study, explicitly stated in the abstract.
Verificationpartial
partial
Evidencepartial
We also report findings on the impact of reasoning trace length on performance and its limitations.
Implicationpartial
This is a specific finding that the paper claims to report.
Verificationpartial
partial
Evidencepartial
and its limitations.
Implicationpartial
This is a specific limitation that the paper claims to report.
Verificationpartial
partial
Evidencepartial
Lastly, we open-source our models and datasets on https://huggingface.co/nri-ai .
Implicationpartial
This is a direct statement about the availability of resources, including a URL.
Verificationpartial
partial

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Constructing Synthetic Instruction Datasets for Improving Reasoning in Domain-Specific LLMs: A Case Study in the Japanese Financial Domain

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