Johannes Bjerva

Research vision

Language as a foundation for trustworthy AI.

I conduct fundamental research into how language-based AI systems encode meaning, generalise across languages and cultures, and fail. These insights inform work on trustworthy AI across languages and contexts.

A connected research programme

From linguistic foundations to trustworthy AI.

  1. Linguistic foundations

    How do models encode meaning and generalise across languages and cultures?

  2. Trustworthy systems

    How can linguistic insight explain failures in security, privacy, safety, and factuality—and support defensible evaluation?

  3. Next frontier

    How do retrieval, tools, memory, and interaction reshape meaning and reliability?

Established research

Four research strands anchor the programme.

Two strands address trustworthiness directly; multilingual NLP and semantics provide foundations across the programme.

Research strand

Language-model security and privacy

How language shapes model security and privacy.

My work spans embedding inversion, information leakage, poisoning, and manipulated or machine-generated text across languages.

Core questionHow do linguistic properties shape vulnerabilities, attacks, and defences?

Selected papersChen et al., ACL 2024Chen et al., EMNLP 2026

Research strand

Factuality and evaluation

Reliable AI requires factual grounding and defensible evaluation.

I study how knowledge grounding, data quality, benchmark design, and language sampling shape claims about model factuality and generalisation.

Core questionHow can outputs be grounded in knowledge, and reliability claims tested beyond narrow benchmarks?

Selected papersLavrinovics et al., ICML 2026Ploeger et al., CL 2026

Research strand

Multilingual NLP

Multilingual does not automatically mean inclusive.

My work combines linguistic typology, careful language sampling, and targeted adaptation, with particular attention to low-resource languages.

Core questionWhat determines whether models and findings transfer across languages?

Selected papersBjerva et al., CL 2019Ren et al., EMNLP 2026

Research strand

Formal and computational semantics

Linguistic structure can make model behaviour easier to analyse.

I draw on formal and computational semantics to analyse model behaviour beyond aggregate performance scores.

Core questionWhich aspects of meaning, context, and inference do current models capture—and which do they miss?

Selected papersAbzianidze et al., EACL 2017Fekete et al., EMNLP 2026

Funded research

Projects and programmes

Selected externally and institutionally funded research, the teams it supports, and work linked to each programme. VBN snapshots are current to July 2026.

Single-PI external awards

≈ DKK 25M

Shared or internal programmes

≈ DKK 10M

Full award values; funding shares are shown per project.

Funding

DKK 9.9M

Data Science Investigator (Ascending) · NNF Research Leader Programme

Principal Investigator

Funding share100%

security2025–2030

(LM)²-SEC

Linguistically Motivated Language Model Security

Developing linguistically grounded approaches to security and privacy in language models, including inversion attacks, information leakage, and malicious manipulation across languages.

Funder
Novo Nordisk Foundation
Funded staffing
2 PhDs · 72 PD-months

Linked work

4 linked outputs

The first outputs address personal-information memorisation, extraction from diffusion language models, and semantic leakage from image embeddings, including work appearing at ACL and EMNLP 2026.

Project and linked outputs on VBN

Funding

DKK 6.2M

Sapere Aude: DFF-Research Leader

Principal Investigator

Funding share100%

security2026–2030

TRUST

Building TRUST in Text: Linguistically Motivated Language Model Detection

Investigating whether linguistic signals in generated text can reveal poisoning and support more robust language models.

Funder
Independent Research Fund Denmark · Sapere Aude
Funded staffing
2 PhDs · 12 PD-months

Current status

Started May 2026

The four-year programme began in May 2026. Project-linked research outputs are forthcoming.

Project details on VBN

Funding

DKK 2.4M

Technical AI Safety Research

Principal Investigator

Funding share100%

semantics2026–2028

Formal Semantic Methods for AI Safety

Formal and computational semantics for AI safety

Developing formal and computational semantic methods to analyse and explain language-model behaviour in multilingual settings.

Funder
Coefficient Giving
Funded staffing
36 PD-months

Current status

Started May 2026

The two-year programme began in May 2026. Project-linked research outputs are forthcoming.

Project details on VBN

Funding

~DKK 7M

AI:X Labs · Aalborg University

Lab Director / Principal Investigator · Co-led with Qiongxiu Li

Funding share50% · co-led

security2025–2029

AI:SECURITY

AI-enabled threats and secure AI systems

Combining NLP and cybersecurity research to study AI-enabled threats and the security of language models in societal applications.

Funder
AI:X Labs · Aalborg University
Funded staffing
4 PhDs · main supervisor for 2

Linked work

5 linked outputs

Linked work spans multilingual memorisation, personal-information leakage, privacy-preserving graph aggregation, and safer model sharing, with papers at EMNLP 2025, ACL 2026, and ICASSP 2026.

Project and linked outputs on VBN

Funding

DKK 1.1M

Industrial PhD · Industrial Researcher Programme

Principal Investigator

factuality2024–2027

Guarantees of Factuality in LLM-based Extraction of Financial KPIs

Industrial PhD in collaboration with ALIPES ApS

Research on factual, knowledge-grounded extraction of financial indicators from earnings transcripts and company filings.

Funder
Innovation Fund Denmark
Funded staffing
Industrial PhD

Linked work

1 dataset · 1 paper

The project has delivered HiFi-KPI: a public dataset and accompanying paper for hierarchical KPI extraction from financial filings, linking applied research to reusable infrastructure.

Project and linked outputs on VBN

Funding

DKK 5M

Semper Ardens: Accelerate · Carlsberg Foundation

Principal Investigator

Funding share100%

multilingual2022–2026

Multilingual Modelling for Resource-Poor Languages

Semper Ardens: Accelerate

Fundamental research on linguistic typology, multilingual modelling, and evaluation for languages underserved by current NLP systems.

Funder
Carlsberg Foundation
Funded staffing
3 PhDs · 36 PD-months

Related award

Google Award for Inclusion Research · CREOLE

DKK 425k · 6 PD-months

Funding share100%

Linked work

40 outputs · 35 activities

VBN currently links 40 outputs spanning typological diversity, multilingual embedding inversion, and low-resource evaluation; the latest EMNLP 2026 papers examine activation-based language similarity and information structure in vision-language models.

Project and linked outputs on VBN

Funding

DKK 3M

Villum Synergy · Villum Foundation

Principal Investigator / NLP methods lead

Funding share50% · co-led

education2024–2026

Digital Twins for Abundant Feedback

Novel Feedback Paradigms via Explainable Multilingual NLP

Studying explainable multilingual NLP for scalable, high-quality feedback in education.

Funder
Villum Foundation · Synergy
Funded staffing
24 PD-months

Linked work

13 outputs · 7 media items

Linked work connects synthetic educational feedback with LLM agents and knowledge-grounded factuality, alongside sustained public discussion of AI-supported teaching and assessment.

Project and linked outputs on VBN

Staffing figures describe funded positions; “PD-months” means funded postdoctoral researcher months. VBN totals are a dated snapshot and will grow as new outputs are linked.