Publications (7)
An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making
Tiansi Dong, Henry He, Pietro Liò +1
This paper compares three methodological categories of neural reasoning: LLM reasoning, supervised learning-based reasoning, and explicit model-based reasoning. LLMs remain unrelia…
Word Sense Disambiguation as a Game of Neurosymbolic Darts
Tiansi Dong, Rafet Sifa
Word Sense Disambiguation (WSD) is one of the hardest tasks in natural language understanding and knowledge engineering. The glass ceiling of 80% F1 score is recently achieved thro…
Learning Syllogism with Euler Neural-Networks
Tiansi Dong, Chengjiang Li, Christian Bauckhage +3
Traditional neural networks represent everything as a vector, and are able to approximate a subset of logical reasoning to a certain degree. As basic logic relations are better rep…
Data-driven Machine Learning Cannot Reach Symbolic-level Logical Reasoning -- The Limit of the Scaling Law
Tiansi Dong, Mateja Jamnik, Pietro Liò
By promoting vectors to spheres and enabling explicit model construction, neural networks can perform symbolic-level syllogistic reasoning without training data. We identify two fu…
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making
Zijun Yao, Chengjiang Li, Tiansi Dong +6
Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. Neural EM models learn vector representation of entity descriptions and match entiti…
Sphere Neural-Networks for Rational Reasoning
Tiansi Dong, Mateja Jamnik, Pietro Liò
The success of Large Language Models (LLMs), e.g., ChatGPT, is witnessed by their planetary popularity, their capability of human-like communication, and also by their steadily imp…