6 papers
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Better Later Than Sooner: Neuro-Symbolic Knowledge Graph Construction via Ontology-grounded Post-extraction Correction
Lorenzo Loconte, Timothy Hospedales, Cristina Cornelio
Question answering (QA) is a core challenge in AI, particularly for complex queries requiring multi-hop reasoning across documents, or symbolic operations like aggregation or exhau…
Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering
Mateusz Czyżnikiewicz, Ryszard Tuora, Adam Kozakiewicz +6
Retrieval-Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document chunks. While effective for un…
The Need for Verification in AI-Driven Scientific Discovery
Cristina Cornelio, Takuya Ito, Ryan Cory-Wright +2
Artificial intelligence (AI) is transforming the practice of science. Machine learning and large language models (LLMs) can generate hypotheses at a scale and speed far exceeding t…
Error Detection and Correction for Interpretable Mathematics in Large Language Models
Yijin Yang, Cristina Cornelio, Mario Leiva +1
Recent large language models (LLMs) have demonstrated the ability to perform explicit multi-step reasoning such as chain-of-thought prompting. However, their intermediate steps oft…
Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification
Cristina Cornelio, Flavio Petruzzellis, Pietro Lio
Large Language Models (LLMs) have shown promise as robotic planners but often struggle with long-horizon and complex tasks, especially in specialized environments requiring externa…