WORKSHOP
“Large Language Models and AI Tools for Research practice”
We bring together leading academic scientists and researchers!
November 20th, 2026 1pm - 5pm
Scope of the event
Large language models are rapidly becoming embedded in the scientific workflow, offering capabilities in lit- erature review, writing assistance, code generation, data extraction from published papers, and hypothesis formulation. This workshop provides a critical and practical treatment of these tools, addressing what LLMs actually do, where they are reliable, where they hallucinate, and how researchers can use them effectively without compromising scientific rigour.
The session develops the technical foundations — tokenisation, attention mechanisms, in-context learning, and fine-tuning — at a level sufficient for researchers to make informed judgements about model capabilities and limitations. It then examines practical deployment: prompt engineering for scientific tasks, retrieval-augmented generation (RAG) for literature-grounded answers, and automated information extraction from scientific papers. Specialised scientific language models, including ChemBERTa, MatSciBERT, and Galactica, are assessed against general-purpose LLMs on scientific reasoning benchmarks.
The workshop gives particular attention to hallucination, provenance, and reproducibility concerns that are especially acute in academic contexts. AI-assisted code generation for scientific computing is addressed as a practical tool, with guidance on verification and testing practices. Participants will leave equipped to adopt LLMs and related AI tools thoughtfully and productively, understanding both their power and their limitations in a research setting.
Extended Key Takeaways & Outputs
• Working understanding of LLM architecture, capabilities, and failure modes
• Practical skills in prompt engineering and RAG for scientific literature
• Assessment of specialised scientific language models versus general-purpose LLMs
• Strategies for managing hallucination, provenance, and reproducibility risks
• Guidance on AI-assisted code generation for scientific computing
Agenda & Speakers
13:00–13:05 — Introduction & Objectives
Organizer
· The integration of large language models into the research workflow
· Opportunities and risks for scientific practice
· Workshop objectives: effective, critical, and responsible use of AI tools
13:05–13:35 — Foundations of Large Language Models
Speaker (Academia): N.N.
· Tokenisation, transformers, and attention mechanisms
· In-context learning and emergent capabilities
· What LLMs actually learn—and what they do not
13:35–14:05 — Prompt Engineering and Retrieval-Augmented Generation (RAG)
Speaker (Academia): N.N.
· Designing effective prompts for scientific tasks
· Retrieval-augmented generation for grounded responses
· Applications in literature review and knowledge synthesis
14:05–14:35 — Scientific Language Models: Domain-Specific vs General-Purpose
Speaker (Academia): N.N.
· ChemBERTa, MatSciBERT, Galactica and domain adaptation
· Benchmarking scientific reasoning capabilities
· Trade-offs between specialisation and generality
14:35–14:45 — Break
14:45–15:15 — AI Tools in Research Practice: Writing, Coding, and Data Extraction
Speaker (Industry): N.N.
· AI-assisted scientific writing and summarisation
· Code generation for modelling and data analysis
· Automated extraction of data from publications
15:15–15:45 — Reliability, Hallucination, and Scientific Integrity
Speaker (Industry): N.N.
· Understanding hallucination and failure modes
· Ensuring provenance, traceability, and reproducibility
· Best practices for validation and verification
15:45–16:15 — Integrating LLMs into Research Workflows
Speaker (Policy/Applied Research): N.N.
· Guidelines for responsible AI use in academia
· Implications for peer review, authorship, and evaluation
· Institutional perspectives on AI adoption
16:15–16:55 — Panel Discussion & Strategic Alignment
All Speakers + Moderator
· How should researchers use LLMs without compromising rigour?
· Will AI reshape scientific authorship and credit?
· Balancing productivity gains with risks of misuse
· Future outlook: AI as collaborator vs tool in research
Concluding remarks
Registration
Deadlines:
Early-Bird: until July 15th, 2026
Registration : until September 5th, 2026
The registration takes place online by filling out the form.
The registration and access to the virtual room for workshop is effective only if the fees payment has been confirmed.
Please note that INVIRTA MEMBERS can purchase all our workshops and services with 10% discount . If you desire to get more information on our membership please press here .
Prices* :
Industry: 150 € (early bird**: 125 €)
Academy: 125 € (early bird**: 100 €)
Students***: 90 € (early bird**: 75 €)
* Note that prices are subject to VAT (for European countries, except Germany, provide the VAT number of your organisation to be within the VAT reverse framework payment).
** Until November 15th, 2020
*** Copy of the registration certificate (Ph.D, undergraduates)
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