Some recent advances in reasoning based on analogical proportions
- URL: http://arxiv.org/abs/2212.11717v1
- Date: Thu, 22 Dec 2022 14:10:14 GMT
- Title: Some recent advances in reasoning based on analogical proportions
- Authors: Myriam Bounhas and Henri Prade and Gilles Richard
- Abstract summary: Analogical proportions play a key role in the formalization of analogical inference.
The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost.
- Score: 9.861775841965386
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Analogical proportions compare pairs of items (a, b) and (c, d) in terms of
their differences and similarities. They play a key role in the formalization
of analogical inference. The paper first discusses how to improve analogical
inference in terms of accuracy and in terms of computational cost. Then it
indicates the potential of analogical proportions for explanation. Finally, it
highlights the close relationship between analogical proportions and
multi-valued dependencies, which reveals an unsuspected aspect of the former.
Related papers
- Normalization in Proportional Feature Spaces [49.48516314472825]
normalization plays an important central role in data representation, characterization, visualization, analysis, comparison, classification, and modeling.
The selection of an appropriate normalization method needs to take into account the type and characteristics of the involved features.
arXiv Detail & Related papers (2024-09-17T17:46:27Z) - Similarity-based analogical proportions [0.0]
The purpose of this paper is to build a bridge from similarity to analogical proportions by formulating the latter in terms of the former.
The benefit of this similarity-based approach is that the connection between proportions and similarity is built into the framework.
arXiv Detail & Related papers (2024-02-28T14:31:34Z) - Predicting Text Preference Via Structured Comparative Reasoning [110.49560164568791]
We introduce SC, a prompting approach that predicts text preferences by generating structured intermediate comparisons.
We select consistent comparisons with a pairwise consistency comparator that ensures each aspect's comparisons clearly distinguish differences between texts.
Our comprehensive evaluations across various NLP tasks, including summarization, retrieval, and automatic rating, demonstrate that SC equips LLMs to achieve state-of-the-art performance in text preference prediction.
arXiv Detail & Related papers (2023-11-14T18:51:38Z) - StoryAnalogy: Deriving Story-level Analogies from Large Language Models
to Unlock Analogical Understanding [72.38872974837462]
We evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus.
textscStory Analogy contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory.
We observe that the data in textscStory Analogy can improve the quality of analogy generation in large language models.
arXiv Detail & Related papers (2023-10-19T16:29:23Z) - ARN: Analogical Reasoning on Narratives [13.707344123755126]
We develop a framework that operationalizes dominant theories of analogy, using narrative elements to create surface and system mappings.
We show that while all LLMs can largely recognize near analogies, even the largest ones struggle with far analogies in a zero-shot setting.
arXiv Detail & Related papers (2023-10-02T08:58:29Z) - Beneath Surface Similarity: Large Language Models Make Reasonable
Scientific Analogies after Structure Abduction [46.2032673640788]
The vital role of analogical reasoning in human cognition allows us to grasp novel concepts by linking them with familiar ones through shared relational structures.
This work suggests that Large Language Models (LLMs) often overlook the structures that underpin these analogies.
This paper introduces a task of analogical structure abduction, grounded in cognitive psychology, designed to abduce structures that form an analogy between two systems.
arXiv Detail & Related papers (2023-05-22T03:04:06Z) - ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base [51.777618249271725]
ANALOGYKB is a million-scale analogy knowledge base derived from existing knowledge graphs (KGs)
It identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large language models (LLMs)
arXiv Detail & Related papers (2023-05-10T09:03:01Z) - Counting Like Human: Anthropoid Crowd Counting on Modeling the
Similarity of Objects [92.80955339180119]
mainstream crowd counting methods regress density map and integrate it to obtain counting results.
Inspired by this, we propose a rational and anthropoid crowd counting framework.
arXiv Detail & Related papers (2022-12-02T07:00:53Z) - Galois theory for analogical classifiers [1.7132914341329848]
Analogical proportions are 4-ary relations that read "A is to B as C is to D"
Recent works have highlighted the fact that such relations can support a specific form of inference, called analogical inference.
arXiv Detail & Related papers (2022-05-09T23:03:56Z) - Analogies and Feature Attributions for Model Agnostic Explanation of
Similarity Learners [29.63747822793279]
We propose a method that provides feature attributions to explain the similarity between a pair of inputs as determined by a black box similarity learner.
Here the goal is to identify diverse analogous pairs of examples that share the same level of similarity as the input pair.
We prove that our analogy objective function is submodular, making the search for good-quality analogies efficient.
arXiv Detail & Related papers (2022-02-02T17:28:56Z) - Analogy as Nonparametric Bayesian Inference over Relational Systems [10.736626320566705]
We propose a Bayesian model that generalizes relational knowledge to novel environments by analogically weighting predictions from previously encountered relational structures.
We show that this learner outperforms a naive, theory-based learner on relational data derived from random- and Wikipedia-based systems when experience with the environment is small.
arXiv Detail & Related papers (2020-06-07T14:07:46Z)
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.