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Found 12 papers, 3 papers with code
Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting
However, the imbalanced training dataset leads to poor performance on rare senses and zero-shot senses.
DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction
However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT.
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs.
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
In this paper, we propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class, which indicates a similar level of plausibility.
PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs
To facilitate the evaluation of pruned subgraphs, we also propose a graph attention network (GAT) based module to reason with the subgraph data.
On the Discussion of Large Language Models: Symmetry of Agents and Interplay with Prompts
Two ways has been discussed to unlock the reasoning capability of a large language model.
Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof Generation with Contrastive Stepwise Decoding
Logical reasoning remains a pivotal component within the realm of artificial intelligence.
Multilingual Word Sense Disambiguation with Unified Sense Representation
As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the lexical semantics of words under specific contexts.
MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation
Commonsense reasoning tasks such as commonsense knowledge graph completion and commonsense question answering require powerful representation learning.
NaMemo: Enhancing Lecturers' Interpersonal Competence of Remembering Students' Names
Addressing students by their names helps a teacher to start building rapport with students and thus facilitates their classroom participation.
Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification
In this manner, the influence of bias and noise in the web data can be gradually alleviated, leading to the steadily improving performance of URNet.
Medical Exam Question Answering with Large-scale Reading Comprehension
Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP.
