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Found 29 papers, 3 papers with code
Metaphor Understanding Challenge Dataset for LLMs
Metaphor understanding is therefore an essential task for large language models (LLMs).
Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models
Large language models (LLMs) have performed well on several reasoning benchmarks, including ones that test analogical reasoning abilities.
Grounded learning for compositional vector semantics
This work proposes a way for compositional distributional semantics to be implemented within a spiking neural network architecture, with the potential to address problems in concept binding, and give a small implementation.
Compositional Fusion of Signals in Data Embedding
Embeddings in AI convert symbolic structures into fixed-dimensional vectors, effectively fusing multiple signals.
EXTRACT: Explainable Transparent Control of Bias in Embeddings
We propose EXTRACT: a suite of Explainable and Transparent methods to ConTrol bias in knowledge graph embeddings, so as to assess and decrease the implicit presence of protected information.
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models
In this work, we focus on the ability of a large pretrained vision and language model (CLIP) to encode compositional concepts and to bind variables in a structure-sensitive way (e. g., differentiating ''cube behind sphere'' from ''sphere behind cube'').
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Recent advances in neural metaphor processing: A linguistic, cognitive and social perspective
Metaphor is an indispensable part of human cognition and everyday communication.
Cats climb entails mammals move: preserving hyponymy in compositional distributional semantics
We give a number of proposals for the structure of Compr, based on spiders, cups and caps, and generate a range of composition rules.
Towards logical negation for compositional distributional semantics
The categorical compositional distributional model of meaning gives the composition of words into phrases and sentences pride of place.
Modelling the interplay of metaphor and emotion through multitask learning
Metaphors allow us to convey emotion by connecting physical experiences and abstract concepts.
Compositional Hyponymy with Positive Operators
Language is used to describe concepts, and many of these concepts are hierarchical.
Compositionality for Recursive Neural Networks
This mapping suggests a number of lines of research for both categorical compositional vector space models of meaning and for recursive neural network models of compositionality.
Translating and Evolving: Towards a Model of Language Change in DisCoCat
We unify the product space representation given in (Coecke et al., 2010) and the functorial description in (Kartsaklis et al., 2013), in a way that allows us to view a language as a catalogue of meanings.
Internal Wiring of Cartesian Verbs and Prepositions
Categorical compositional distributional semantics (CCDS) allows one to compute the meaning of phrases and sentences from the meaning of their constituent words.
Proceedings of the 2018 Workshop on Compositional Approaches in Physics, NLP, and Social Sciences
Moreover, the interaction between the three disciplines of cognitive science, linguistics and game theory is a fertile ground for research.
Towards Functorial Language-Games
In categorical compositional semantics of natural language one studies functors from a category of grammatical derivations (such as a Lambek pregroup) to a semantic category (such as real vector spaces).
Interacting Conceptual Spaces I : Grammatical Composition of Concepts
We show how this approach can be generalized to conceptual space models of cognition.
Compositional Distributional Cognition
We accommodate the Integrated Connectionist/Symbolic Architecture (ICS) of [32] within the categorical compositional semantics (CatCo) of [13], forming a model of categorical compositional cognition (CatCog).
Interacting Conceptual Spaces
We propose applying the categorical compositional scheme of [6] to conceptual space models of cognition.
Proceedings of the 2016 Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science
This volume contains the Proceedings of the 2016 Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science (SLPCS 2016), which was held on the 11th of June at the University of Strathclyde, Glasgow, and was co-located with Quantum Physics and Logic (QPL 2016).
Harmonic Grammar in a DisCo Model of Meaning
The model of cognition developed in (Smolensky and Legendre, 2006) seeks to unify two levels of description of the cognitive process: the connectionist and the symbolic.
A Label Semantics Approach to Linguistic Hedges
We introduce a model for the linguistic hedges `very' and `quite' within the label semantics framework, and combined with the prototype and conceptual spaces theories of concepts.
Concept Generation in Language Evolution
This thesis investigates the generation of new concepts from combinations of existing concepts as a language evolves.
Emerging Dimension Weights in a Conceptual Spaces Model of Concept Combination
The expected value and the variance of these weights across agents may be predicted from the distribution of elements in the conceptual space, as determined by the underlying environment, together with the rate at which agents adopt others' concepts.
The Utility of Hedged Assertions in the Emergence of Shared Categorical Labels
Results show that using hedged assertions positively affects the emergence of shared categories in two distinct ways.
Graded Entailment for Compositional Distributional Semantics
In this paper we solve the problem of entailment for categorical compositional distributional semantics.
A Compositional Explanation of the Pet Fish Phenomenon
The `pet fish' phenomenon is often cited as a paradigm example of the `non-compositionality' of human concept use.
