Mathematical Induction
4 papers with code • 1 benchmarks • 1 datasets
Tests the language model's capability to understand induction by asking the model to verify the correctness of an induction argument.
Source: BIG-bench
Benchmarks
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BIG-bench
Most implemented papers
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
deep-symbolic-mathematics/Multimodal-Math-Pretraining
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To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings.
Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification
This paper presents a novel approach to binary classification using dynamic logistic ensemble models.
