Abstract Reasoning via Logic-guided Generation

• URL: http://arxiv.org/abs/2107.10493v1
• Date: Thu, 22 Jul 2021 07:28:24 GMT
• Title: Abstract Reasoning via Logic-guided Generation
• Authors: Sihyun Yu, Sangwoo Mo, Sungsoo Ahn, Jinwoo Shin
• Abstract summary: Abstract reasoning, i.e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence. This paper aims to design a framework for the latter approach and bridge the gap between artificial and human intelligence. We propose logic-guided generation (LoGe), a novel generative DNN framework that reduces abstract reasoning as an optimization problem in propositional logic.
• Score: 65.92805601327649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Abstract reasoning, i.e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence. While humans find the answer by either eliminating wrong candidates or first constructing the answer, prior deep neural network (DNN)-based methods focus on the former discriminative approach. This paper aims to design a framework for the latter approach and bridge the gap between artificial and human intelligence. To this end, we propose logic-guided generation (LoGe), a novel generative DNN framework that reduces abstract reasoning as an optimization problem in propositional logic. LoGe is composed of three steps: extract propositional variables from images, reason the answer variables with a logic layer, and reconstruct the answer image from the variables. We demonstrate that LoGe outperforms the black box DNN frameworks for generative abstract reasoning under the RAVEN benchmark, i.e., reconstructing answers based on capturing correct rules of various attributes from observations.

Related papers

• P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains [97.25943550933829]
  We present P-FOLIO, a human-annotated dataset consisting of diverse and complex reasoning chains. We use P-FOLIO to evaluate and improve large-language-model (LLM) reasoning capabilities.
  arXiv  Detail & Related papers  (2024-10-11T19:22:57Z)
• Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension [9.67774998354062]
  Previous research has primarily focused on enhancing logical reasoning capabilities through Chain-of-Thought (CoT) or data augmentation. We propose a Premise-Oriented Data Augmentation (PODA) framework to generate CoT rationales including analyses for both correct and incorrect options. We also introduce a novel thought-path contrastive learning method that compares reasoning paths between the original and counterfactual samples.
  arXiv  Detail & Related papers  (2024-09-22T15:44:43Z)
• Language Models can be Logical Solvers [99.40649402395725]
  We introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers.
  arXiv  Detail & Related papers  (2023-11-10T16:23:50Z)
• LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning [73.98142349171552]
  LOGICSEG is a holistic visual semantic that integrates neural inductive learning and logic reasoning with both rich data and symbolic knowledge. During fuzzy logic-based continuous relaxation, logical formulae are grounded onto data and neural computational graphs, hence enabling logic-induced network training. These designs together make LOGICSEG a general and compact neural-logic machine that is readily integrated into existing segmentation models.
  arXiv  Detail & Related papers  (2023-09-24T05:43:19Z)
• LAMBADA: Backward Chaining for Automated Reasoning in Natural Language [11.096348678079574]
  Backward Chaining algorithm, called LAMBADA, decomposes reasoning into four sub-modules. We show that LAMBADA achieves sizable accuracy boosts over state-of-the-art forward reasoning methods.
  arXiv  Detail & Related papers  (2022-12-20T18:06:03Z)
• MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure [129.8481568648651]
  We propose a benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Based on the multi-hop chain of reasoning, the explanation form includes three main components. We evaluate the current best models' performance on this new explanation form.
  arXiv  Detail & Related papers  (2022-10-22T16:01:13Z)
• Joint Abductive and Inductive Neural Logical Reasoning [44.36651614420507]
  We formulate the problem of the joint abductive and inductive neural logical reasoning (AI-NLR) First, we incorporate description logic-based ontological axioms to provide the source of concepts. Then, we represent concepts and queries as fuzzy sets, i.e., sets whose elements have degrees of membership, to bridge concepts and queries with entities.
  arXiv  Detail & Related papers  (2022-05-29T07:41:50Z)
• LOREN: Logic Enhanced Neural Reasoning for Fact Verification [24.768868510218002]
  We propose LOREN, a novel approach for fact verification that integrates Logic guided Reasoning and Neural inference. Instead of directly validating a single reasoning unit, LOREN turns it into a question-answering task. Experiments show that our proposed LOREN outperforms other previously published methods and achieves 73.43% of the FEVER score.
  arXiv  Detail & Related papers  (2020-12-25T13:57:04Z)
• RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs [91.71504177786792]
  This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks. Existing methods either suffer from the problem of searching in a large search space or ineffective optimization due to sparse rewards.
  arXiv  Detail & Related papers  (2020-10-08T14:47:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.