Program induction
22 papers with code • 0 benchmarks • 1 datasets
Generating program code for domain-specific tasks
Benchmarks
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Latest papers
KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases
Secondly, KB-Plugin utilizes abundant annotated data from a rich-resourced KB to train another pluggable module, namely PI plugin, which can help the LLM extract question-relevant schema information from the schema plugin of any KB and utilize this information to induce programs over this KB.
Learning Differentiable Logic Programs for Abstract Visual Reasoning
However, due to the memory intensity, most existing approaches do not bring the best of the expressivity of first-order logic, excluding a crucial ability to solve abstract visual reasoning, where agents need to perform reasoning by using analogies on abstract concepts in different scenarios.
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key.
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering
Question answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema linking.
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?
Large language models have recently shown a remarkable ability for few-shot learning, including patterns of algorithmic nature.
Map Induction: Compositional spatial submap learning for efficient exploration in novel environments
Humans are expert explorers.
Program Transfer for Answering Complex Questions over Knowledge Bases
In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations.
Learning a Deep Generative Model like a Program: the Free Category Prior
Humans surpass the cognitive abilities of most other animals in our ability to "chunk" concepts into words, and then combine the words to combine the concepts.
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set.
Strong Generalization and Efficiency in Neural Programs
We study the problem of learning efficient algorithms that strongly generalize in the framework of neural program induction.