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Found 7 papers, 5 papers with code
Solving Math Word Problems by Combining Language Models With Symbolic Solvers
Automatically generating high-quality step-by-step solutions to math word problems has many applications in education.
Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
Despite recent success in large language model (LLM) reasoning, LLMs struggle with hierarchical multi-step reasoning tasks like generating complex programs.
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Peano: Learning Formal Mathematical Reasoning
We explore this idea in a case study on 5 sections of beginning algebra on the Khan Academy platform.
LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions
Humans tame the complexity of mathematical reasoning by developing hierarchies of abstractions.
Synchromesh: Reliable code generation from pre-trained language models
Then, Synchromesh feeds the examples to a pre-trained language model and samples programs using Constrained Semantic Decoding (CSD): a general framework for constraining the output to a set of valid programs in the target language.
Open-domain clarification question generation without question examples
An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans.
Contrastive Reinforcement Learning of Symbolic Reasoning Domains
Our results suggest new directions for reinforcement learning in symbolic domains, as well as applications to mathematics education.
