A key question about LLMs is whether they solve reasoning tasks by learning transferable algorithms or simply memorizing training data. This distinction matters: while memorization might handle ...

This repository contains the code for ACL 2024 paper: An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs. How to Cite: If you ...

Transformer models have significantly advanced machine learning, particularly in handling complex tasks such as natural language processing and arithmetic operations like addition and multiplication.

Recent advancements in Large Language Models (LLMs) have showcased striking results on existing logical reasoning benchmarks, with some models even surpassing human performance. However, the true ...

Researchers have conducted a systematic analysis of large language models' capabilities for inductive and deductive reasoning. The study reveals both surprising strengths and clear limitations of ...

This study investigated the developmental relationship between children's mental representations of the arithmetic change problem structure (the change problem schema) and their use of part-whole ...