team
Camille Roth
- Research director
- Formalization
Telmo Menezes
- Research
- Technical architecture
- Neuro-symbolic modeling
Florian Schmitt
- UX / UI Design
- Ergonomic interface design
- Front-end integration
project origin
context and challenges
Information management
spreadsheets, relational databases
Information extraction
turning text into structured data
Desire for automation
AI boom raised organizational awareness
outstanding needs
- Lack of internal AI expertise Difficulty in evaluating or negotiating services
- Solutions not tailored to specific needs Uncertain cost-benefit analysis
- Lack of trust in LLMs Hallucinations, opacity
interface between two canonical approaches
Top-down
symbolic rules
explicit specification of target regularities
- High precision
- Low recall
- Explainable
- Rigid
- Requires few
examples
Bottom-up
deep learning, LLMs
implicitly captures regularities with numerous annotated examples
- High recall
- Enhanced handling
of context
- Black box
- Costly training
- Limited ability to
explore data
hyperquest
best of both worlds
HyperQuest
reveals key patterns
Allows domain experts without AI expertise to select the most relevant cases
Suggests/co-constructs targeted information extraction rules with few examples
Expands assisted extraction through neuro-symbolic patterns
successive refinement of sub-corpora and increasingly relevant patterns
partners

partly funded by ERC Proof-of-Concept
grant « HyperQuest » #101292712
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