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Found 6 papers, 3 papers with code
Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs
More specifically, we employ an LLM to generate explanations for a set of <problem, solution-program> pairs, then use <problem, explanation> pairs to fine-tune a smaller language model, which we refer to as the Reasoner, to learn algorithmic reasoning that can generate "how-to-solve" hints for unseen problems.
ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models
In recent times, large language models (LLMs) have shown impressive performance on various document-level tasks such as document classification, summarization, and question-answering.
Explaining Competitive-Level Programming Solutions using LLMs
In this paper, we approach competitive-level programming problem-solving as a composite task of reasoning and code generation.
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction
Solving math word problems requires deductive reasoning over the quantities in the text.
Ranked #4 on
Math Word Problem Solving
on MathQA
Evaluating Explanation Methods for Neural Machine Translation
Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods.
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions
Several deep learning models have been proposed for solving math word problems (MWPs) automatically.
Ranked #13 on
Math Word Problem Solving
on Math23K
