Thanks to the jury members for having evaluated this work: Elena Demidov, Enrico Motta, Serena Villata, @papotti.bsky.social / co supervised with Pasquale Lisena

The final contribution is a rule-based system that again makes use of fine-grained causal relationships between events for explaining fact-checking results. Evaluated on the #FEVEROUS and #AVeriTeC datasets.

A third contribution is a method that relies on the JoinGT system but incorporates fine-grained causal relationships between events from FARO for generating narratives and stories of events. We demonstrated that qualitatively, the generated summaries are more complete.

This dataset can then be used to fine-tune language models (RoBERTa and REBEL) to be more effective at detecting fine-grained causal relationships between events in texts.
A demonstrator making use of these models is available at demo.kflow.eurecom.fr

A second major contribution is the largest dataset containing fine-grained annotations of causal relationships between events (enable, prevent, intend, directly cause). This dataset comes from common sense (ATOMIC), news corpus (CNC) and synthetic #LLM generated data.

Youssra also investigated how capable #LLMs are for reverse-engineering competency questions and deriving user stories out of formal ontologies. Results demonstrate that recent large models such as GPT4 are effective for general domain.

A first contribution is the Facts and Events Relationships Ontology (FARO) that harmonizes different models representing event relations. This is available at anr-kflow.github.io/faro/

The tool provides an intuitive way of visualizing this data:
✅ Explore electricity consumption across France with an interactive map.
✅ Analyze trends over time with detailed graphs and insights.
✅ Discover similarities between territories.
Check out conso-explorer.tools.eurecom.fr

More about the challenge: frugal-ai-challenge.github.io
What’s more? We open source our winning solution and alternative attempts! Check out github.com/D2KLab/fruga...