ARXIV:2602.16811 · QUESTION ANSWERING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark

arXiv

Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages. Despite these advancements, research on LLMs has primarily targeted high-resourced languages (e.g., English), and only…

METHOD

Full abstract

Recent advancements in Natural Language Processing and Deep Learning have enabled the development of Large Language Models (LLMs), which have significantly advanced the state-of-the-art across a wide range of tasks, including Question Answering (QA). Despite these advancements, research on LLMs has primarily targeted high-resourced languages (e.g., English), and only recently has attention shifted toward multilingual models. However, these models demonstrate a training data bias towards a small number of popular languages or rely on transfer learning from high- to under-resourced languages; this may lead to a misrepresentation of social, cultural, and historical aspects. To address this challenge, monolingual LLMs have been developed for under-resourced languages; however, their effectiveness remains less studied when compared to multilingual counterparts on language-specific tasks. In this study, we address this research gap in Greek QA by contributing: (i) DemosQA, a novel dataset, which is constructed using social media user questions and community-reviewed answers to better capture the Greek social and cultural zeitgeist; (ii) a memory-efficient LLM evaluation framework adaptable to diverse QA datasets and languages; and (iii) an extensive evaluation of 11 monolingual and multilingual LLMs on 6 human-curated Greek QA datasets using 3 different prompting strategies. We release our code and data to facilitate reproducibility.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. However, these models demonstrate a training data bias towards a small number of popular languages or rely on transfer learning from high- to under-resourced…

WHY NOW

Question Answering moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainDevelop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

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

ARXIV:2602.16811 · QUESTION ANSWERING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark

arXiv

Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Question Answering moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainDevelop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

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

Paper Pack

10.48550/arXiv.2602.16811

Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark

Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

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 fallback30 indexednot indexed

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

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Question Answering moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages. Despite these advancements, research on LLMs has primarily targeted high-resourced languages (e.g., English), and only recently has attention shifted toward multilingual models.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Recent advancements in Natural Language Processing and Deep Learning have enabled the development of Large Language Models (LLMs), which have significantly advanced the state-of-the-art across a wide range of tasks, including Question Answering (QA). Despite these advancements, research on LLMs has primarily targeted high-resourced languages (e.g., English), and only recently has attention shifted toward multilingual models.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. However, these models demonstrate a training data bias towards a small number of popular languages or rely on transfer learning from high- to under-resourced languages; this may lead to a misrepresentation of social, cultural, and historical aspects.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Question Answering moved forward this cycle; last verified April 2026. Public score 5.0/10.
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

Question Answering

Competitors

not indexed

Competitive landscape

Develop a Greek-specific QA system leveraging a new dataset and evaluation framework for enhancing LLM performance in under-resourced languages.

Segment

Question Answering

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.16811 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

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PDF

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

References(30)

GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citations

2025Odysseas S. Chlapanis, D. Galanis et al.

Command A: An Enterprise-Ready Large Language Model

2025Team Cohere, Aakanksha et al.

Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance

2025Xueqing Peng, Triantafillos Papadopoulos et al.

Open or Closed LLM for Lesser-Resourced Languages? Lessons from Greek

2025John Pavlopoulos, Juli Bakagianni et al.

Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier

2024John Dang, Shivalika Singh et al.

INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge

2024Angelika Romanou, Negar Foroutan et al.

Greek Wikipedia: A Study on Abstractive Summarization

2024Nikolaos Giarelis, Charalampos Mastrokostas et al.

Social Media Topic Classification on Greek Reddit

2024Charalampos Mastrokostas, Nikolaos Giarelis et al.

NLP for The Greek Language: A Longer Survey

2024Katerina Papantoniou, Yannis Tzitzikas

The Llama 3 Herd of Models

2024Abhimanyu Dubey, Abhinav Jauhri et al.

Gemma 2: Improving Open Language Models at a Practical Size

2024Gemma Team Morgane Riviere, Shreya Pathak et al.

Meltemi: The first open Large Language Model for Greek

2024Leon Voukoutis, Dimitris Roussis et al.

A systematic survey of natural language processing for the Greek language

2024Juli Bakagianni, K. Pouli et al.

The Effect of Sampling Temperature on Problem Solving in Large Language Models

2024Matthew Renze, Erhan Guven

A Study on Large Language Models' Limitations in Multiple-Choice Question Answering

2024Aisha Khatun, Daniel G. Brown

GreekT5: Sequence-to-Sequence Models for Greek News Summarization

2024Nikolaos Giarelis, Charalampos Mastrokostas et al.

A Review of Greek NLP Technologies for Chatbot Development

2023Nikolaos Giarelis, Charalampos Mastrokostas et al.

The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants

2023Lucas Bandarkar, Davis Liang et al.

How is ChatGPT's behavior changing over time?

2023Lingjiao Chen, Matei Zaharia et al.

GPT-4 Technical Report

2023OpenAI Josh Achiam, Steven Adler et al.

Showing 20 of 30 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

Prior WorkCan Language Models Analyze Data? Evaluating Large Language Models for Question Answering over Datasets

5.0

Prior WorkBeyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering

5.0

Prior WorkMultilingual Large Language Models do not comprehend all natural languages to equal degrees

5.0

Prior WorkMultilingual Training and Evaluation Resources for Vision-Language Models

5.0

Extension

Builds On ThisImproving Answer Extraction in Context-based Question Answering Systems Using LLMs

0.0

Commercially relevant

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

7.0

Higher ViabilityLocation Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs

7.0

Higher ViabilityAutomating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models

7.0

Higher ViabilityGaelEval: Benchmarking LLM Performance for Scottish Gaelic

7.0

Higher ViabilityBeyond Questions: Evaluating What Large Language Models (Actually) Know

8.0

Conflicting

none indexed

Related Resources

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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: 2602.16811
Route: /paper/evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark
Active tab: read
Artifact: evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark/paper-pack
REST build passport API/api/v1/paper/evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark/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/evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
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

Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark

Canonical ID evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark | Route /paper/evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark

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: Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark

/buildability/evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark

Watchwatch

Subject: Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark

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/evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark

Paper ref

evaluating-monolingual-and-multilingual-large-language-models-for-greek-question-answering-the-demosqa-benchmark

arXiv id

2602.16811

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

fc26b1d94b37860badde0660941805e7d6517421cb20258be9b3ee52da223a9c

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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Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark

Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark

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