Analogical Proportions and Creativity: A Preliminary Study
- URL: http://arxiv.org/abs/2310.13500v1
- Date: Fri, 20 Oct 2023 13:46:04 GMT
- Title: Analogical Proportions and Creativity: A Preliminary Study
- Abstract summary: Analogical proportions are statements of the form "$a$ is to $b$ as $c$ is to $d$"
We try to "create" new animals from existing ones, retrieving rare animals such as platypus.
- Score: 4.005483185111992
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Analogical proportions are statements of the form "$a$ is to $b$ as $c$ is to
$d$", which expresses that the comparisons of the elements in pair $(a, b)$ and
in pair $(c, d)$ yield similar results. Analogical proportions are creative in
the sense that given 3 distinct items, the representation of a 4th item $d$,
distinct from the previous items, which forms an analogical proportion with
them can be calculated, provided certain conditions are met. After providing an
introduction to analogical proportions and their properties, the paper reports
the results of an experiment made with a database of animal descriptions and
their class, where we try to "create" new animals from existing ones,
retrieving rare animals such as platypus. We perform a series of experiments
using word embeddings as well as Boolean features in order to propose novel
animals based on analogical proportions, showing that word embeddings obtain
better results.
Related papers
- How do Transformer Embeddings Represent Compositions? A Functional Analysis [5.0310636905746975]
We test compositionality in Mistral, OpenAI Large, and Google embedding models, and compare them with BERT.<n>We find that ridge regression, albeit linear, best accounts for compositionality.<n>Surprisingly, we find that the classic vector addition model performs almost as well as any other model.
arXiv Detail & Related papers (2025-06-01T09:02:56Z) - On the Emergence of Linear Analogies in Word Embeddings [5.440589713820591]
Models such as Word2Vec and GloVe construct word embeddings based on the co-occurrence probability $P(i,j)$ of words $i$ and $j$ in text corpora.<n>We introduce a theoretical generative model in which words are defined by binary semantic attributes, and co-occurrence probabilities are derived from attribute-based interactions.
arXiv Detail & Related papers (2025-05-24T11:42:26Z) - Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related tasks [59.47851630504264]
Free-text explanations are expressive and easy to understand, but many datasets lack annotated explanation data.
We fine-tune T5-Large and OLMo-7B models and assess the impact of fine-tuning data quality, the number of fine-tuning samples, and few-shot selection methods.
The models are evaluated on 19 diverse OOD datasets across three tasks: natural language inference (NLI), fact-checking, and hallucination detection in abstractive summarization.
arXiv Detail & Related papers (2025-02-07T10:01:32Z) - Relative Value Biases in Large Language Models [0.0]
Studies of reinforcement learning in humans and animals have demonstrated a preference for options that yielded relatively better outcomes in the past.
The present study tested whether large language models would exhibit a similar bias.
arXiv Detail & Related papers (2024-01-25T21:49:32Z) - Learning Part Segmentation from Synthetic Animals [11.350951919924455]
This paper focuses on learning part segmentation from synthetic animals, leveraging the Skinned Multi-Animal Linear (SMAL) models.
We benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods.
Remarkably, our third contribution is to reveal that the learned parts from synthetic tiger and horse are transferable across all quadrupeds in PartImageNet.
arXiv Detail & Related papers (2023-11-30T16:10:04Z) - 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) - Proportoids [0.0]
Analogical proportions are expressions of the form $a$ is to $b$ what $c$ is to $d$'' at the core of analogical reasoning.
This paper contributes to the mathematical foundations of analogical proportions in the axiomatic tradition.
arXiv Detail & Related papers (2022-08-31T00:04:34Z) - A Cascade Model for Argument Mining in Japanese Political Discussions:
the QA Lab-PoliInfo-3 Case Study [5.482532589225552]
The rVRAIN team tackled the Budget Argument Mining (BAM) task, consisting of a combination of classification and information retrieval sub-tasks.
For the argument classification (AC), the team achieved its best performing results with a five-class BERT-based cascade model complemented with some handcrafted rules.
For the information retrieval (i.e., relation ID detection or RID), our best results were achieved by a combination of a BERT-based binary classifier, and the cosine similarity of pairs consisting of the monetary expression and budget dense embeddings.
arXiv Detail & Related papers (2022-07-04T18:49:18Z) - On the Generative Utility of Cyclic Conditionals [103.1624347008042]
We study whether and how can we model a joint distribution $p(x,z)$ using two conditional models $p(x|z)$ that form a cycle.
We propose the CyGen framework for cyclic-conditional generative modeling, including methods to enforce compatibility and use the determined distribution to fit and generate data.
arXiv Detail & Related papers (2021-06-30T10:23:45Z) - Contrastive Explanations for Model Interpretability [77.92370750072831]
We propose a methodology to produce contrastive explanations for classification models.
Our method is based on projecting model representation to a latent space.
Our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.
arXiv Detail & Related papers (2021-03-02T00:36:45Z) - Exploring and Evaluating Attributes, Values, and Structures for Entity
Alignment [100.19568734815732]
Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs.
attribute triples can also provide crucial alignment signal but have not been well explored yet.
We propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently.
arXiv Detail & Related papers (2020-10-07T08:03:58Z) - Improving Robustness and Generality of NLP Models Using Disentangled
Representations [62.08794500431367]
Supervised neural networks first map an input $x$ to a single representation $z$, and then map $z$ to the output label $y$.
We present methods to improve robustness and generality of NLP models from the standpoint of disentangled representation learning.
We show that models trained with the proposed criteria provide better robustness and domain adaptation ability in a wide range of supervised learning tasks.
arXiv Detail & Related papers (2020-09-21T02:48:46Z) - Few-shot Visual Reasoning with Meta-analogical Contrastive Learning [141.2562447971]
We propose to solve a few-shot (or low-shot) visual reasoning problem, by resorting to analogical reasoning.
We extract structural relationships between elements in both domains, and enforce them to be as similar as possible with analogical learning.
We validate our method on RAVEN dataset, on which it outperforms state-of-the-art method, with larger gains when the training data is scarce.
arXiv Detail & Related papers (2020-07-23T14:00:34Z) - Logical Separability of Labeled Data Examples under Ontologies [23.800397174740986]
We investigate the existence of a separating formula for data in the presence of an ontology.
For separation, we consider the description logic $mathcalALCI$, the guarded fragment, the two-variable fragment, and the guarded fragment.
Our results are model-theoretic characterizations of (all variants of) separability, the comparison of the separating power of different languages, and the investigation of the computational complexity of deciding separability.
arXiv Detail & Related papers (2020-07-03T11:00:47Z)
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.