Neural nets that do symbolic maths

December 9, 2019 — June 14, 2023

compsci
language
machine learning
meta learning
networks
neural nets
NLP
stringology

Somewhere between computational symbolic mathematics and automated proof assistants and the modern large language models are models that can solve mathematical problems more effectively than my feeble brain.

Watch this space.

Figure 1

1 Incoming

2 References

Bubeck, Chandrasekaran, Eldan, et al. 2023. “Sparks of Artificial General Intelligence: Early Experiments with GPT-4.”
Clark, Tafjord, and Richardson. 2020. “Transformers as Soft Reasoners over Language.” In IJCAI 2020.
Fu, Ou, Chen, et al. 2023. “Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models’ Reasoning Performance.”
Garcez, and Lamb. 2020. “Neurosymbolic AI: The 3rd Wave.”
Lample, and Charton. 2019. “Deep Learning for Symbolic Mathematics.” arXiv:1912.01412 [Cs].
Mirzadeh, Alizadeh, Shahrokhi, et al. 2024. “GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.”
Radford, Wu, Child, et al. 2019. “Language Models Are Unsupervised Multitask Learners.”
Zhang, Backurs, Bubeck, et al. 2022. “Unveiling Transformers with LEGO: A Synthetic Reasoning Task.”