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Found 7 papers, 3 papers with code
CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models
We comprehensively evaluated CodeTree on 7 code generation benchmarks and demonstrated the significant performance gains of CodeTree against strong baselines.
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 #14 on
Math Word Problem Solving
on Math23K
