ARXIV:2603.02041 · MULTILINGUAL LLM ENHANCEMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

arXiv

EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance. We study whether continued pretraining (CPT) can substantially improve Estonian capabilities in a pretrained multilingual LLM while…

METHOD

Full abstract

Large language models (LLMs) are predominantly trained on English-centric data, resulting in uneven performance for smaller languages. We study whether continued pretraining (CPT) can substantially improve Estonian capabilities in a pretrained multilingual LLM while preserving its English and general reasoning performance. Using Llama 3.1 8B as the main base model, we perform CPT on a mixture that increases Estonian exposure while approximating the original training distribution through English replay and the inclusion of code, mathematics, and instruction-like data. We subsequently apply supervised fine-tuning, preference optimization, and chat vector merging to introduce robust instruction-following behavior. Evaluation on a comprehensive suite of Estonian benchmarks shows consistent gains in linguistic competence, knowledge, reasoning, translation quality, and instruction-following compared to the original base model and its instruction-tuned variant, while maintaining competitive performance on English benchmarks. These findings indicate that CPT, with an appropriately balanced data mixture, together with post-training alignment, can substantially improve single-language capabilities in pretrained multilingual LLMs.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. We study whether continued pretraining (CPT) can substantially improve Estonian capabilities in a pretrained multilingual LLM while preserving its English and general reasoning performance.

WHY NOW

Multilingual LLM Enhancement moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainEstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.02041 · MULTILINGUAL LLM ENHANCEMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

arXiv

EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Multilingual LLM Enhancement moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainEstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.02041

EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback53 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

6.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Multilingual LLM Enhancement moved forward this cycle; last verified April 2026. Public score 6.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance. We study whether continued pretraining (CPT) can substantially improve Estonian capabilities in a pretrained multilingual LLM while preserving its English and general reasoning performance.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Large language models (LLMs) are predominantly trained on English-centric data, resulting in uneven performance for smaller languages. We study whether continued pretraining (CPT) can substantially improve Estonian capabilities in a pretrained multilingual LLM while preserving its English and general reasoning performance.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We study whether continued pretraining (CPT) can substantially improve Estonian capabilities in a pretrained multilingual LLM while preserving its English and general reasoning performance.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Multilingual LLM Enhancement moved forward this cycle; last verified April 2026. Public score 6.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Multilingual LLM Enhancement

Competitors

not indexed

Competitive landscape

EstLLM enhances Estonian language capabilities in multilingual LLMs through continued pretraining and strategic post-training, improving language-specific performance.

Segment

Multilingual LLM Enhancement

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.02041 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(53)

AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages

2026Hao Yu, Tianyi Xu et al.

Estonian Native Large Language Model Benchmark

2025Helena Grete Lillepalu, Tanel Alumäe

FineWeb2: One Pipeline to Scale Them All - Adapting Pre-Training Data Processing to Every Language

2025Guilherme Penedo, Hynek Kydlícek et al.

Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque

2025Oscar Sainz, Naiara Pérez et al.

MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation

2025Yile Liu, Ziwei Ma et al.

HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages

2025Zhilin Wang, Jiaqi Zeng et al.

Rewriting Pre-Training Data Boosts LLM Performance in Math and Code

2025Kazuki Fujii, Yukito Tajima et al.

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation

2025Weihao Xuan, Rui Yang et al.

Prune or Retrain: Optimizing the Vocabulary of Multilingual Models for Estonian

2025Aleksei Dorkin, Taido Purason et al.

Continued Pretraining and Interpretability-Based Evaluation for Low-Resource Languages: A Galician Case Study

2025Pablo Rodríguez, Silvia Paniagua Suárez et al.

Language Adaptation of Large Language Models: An Empirical Study on LLaMA2

2025Shumin Wang, Yuexiang Xie et al.

DIPLomA: Efficient Adaptation of Instructed LLMs to Low-Resource Languages via Post-Training Delta Merging

2025Ixak Sarasua, Ander Corral et al.

Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque

2024Ander Corral, Ixak Sarasua et al.

Small Languages, Big Models: A Study of Continual Training on Languages of Norway

2024David Samuel, Vladislav Mikhailov et al.

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

2024Shivalika Singh, Angelika Romanou et al.

LLMs for Extremely Low-Resource Finno-Ugric Languages

2024Taido Purason, Hele-Andra Kuulmets et al.

Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpus

2024Raviraj Joshi, Kanishk Singla et al.

To Code, or Not To Code? Exploring Impact of Code in Pre-training

2024Viraat Aryabumi, Yixuan Su et al.

Instruction Pre-Training: Language Models are Supervised Multitask Learners

2024Daixuan Cheng, Yuxian Gu et al.

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

2024Zhangchen Xu, Fengqing Jiang et al.

Showing 20 of 53 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisEnglish is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training

3.0

Builds On ThisMerge and Conquer: Instructing Multilingual Models by Adding Target Language Weights

5.0

Builds On ThisCan Linguistically Related Languages Guide LLM Translation in Low-Resource Settings?

5.0

Builds On ThisContinual Learning in Large Language Models: Methods, Challenges, and Opportunities

3.0

Builds On ThisOptimizing Multilingual LLMs via Federated Learning: A Study of Client Language Composition

3.0

Builds On ThisBack to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models

3.0

Commercially relevant

Higher ViabilityPositional Cognitive Specialization: Where Do LLMs Learn To Comprehend and Speak Your Language?

7.0

Higher ViabilityTowards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study

7.0

Higher ViabilityTildeOpen LLM: Leveraging Curriculum Learning to Achieve Equitable Language Representation

8.0

Higher ViabilityLanguage-Aware Distillation for Multilingual Instruction-Following Speech LLMs with ASR-Only Supervision

7.0

Conflicting

none indexed

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.02041
Route: /paper/estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training
Active tab: read
Artifact: estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training/paper-pack
REST build passport API/api/v1/paper/estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training

stale

Proof freshness: stale
Proof status: unverified
Display score: 6/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

Canonical ID estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training | Route /paper/estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training

MCP example

{
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}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

/buildability/estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training

Watchwatch

Subject: EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training

Paper ref

estllm-enhancing-estonian-capabilities-in-multilingual-llms-via-continued-pretraining-and-post-training

arXiv id

2603.02041

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

4c223864165e9fa7fce6def8823504fc952d76d7316bacdf1d5ad29c13fecef2

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

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References(53)

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Brief

Experiment plan

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Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline