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Found 6 papers, 1 papers with code
Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge
Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively.
MLPs Compass: What is learned when MLPs are combined with PLMs?
While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain derived from the additional components of PLMs remains an open question in this field.
Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
Document-level event argument extraction is a crucial yet challenging task within the field of information extraction.
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance
Document-level event argument extraction poses new challenges of long input and cross-sentence inference compared to its sentence-level counterpart.
Ranked #1 on
Event Argument Extraction
on WikiEvents
(F1 metric)
Document-Level Relation Extraction with Structure Enhanced Transformer Encoder
However, previous transformer-based methods neglect structural information between entities, while graph-based methods are unable to extract structural information effectively on account that they isolate the en-coding stage and structure reasoning stage.
Ranked #8 on
Relation Extraction
on DocRED
DPGNN: Dual-Perception Graph Neural Network for Representation Learning
To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction.
