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Found 8 papers, 3 papers with code
CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation
In this paper, we propose a document-level neural machine translation framework, CoDoNMT, which models cohesion devices from two perspectives: Cohesion Device Masking (CoDM) and Cohesion Attention Focusing (CoAF).
LHMKE: A Large-scale Holistic Multi-subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Current datasets collect questions from Chinese examinations across different subjects and educational levels to address this issue.
Do Large Language Models Mirror Cognitive Language Processing?
We employ Representational Similarity Analysis (RSA) to mearsure the alignment between 16 mainstream LLMs and fMRI signals of the brain.
AI-driven emergence of frequency information non-uniform distribution via THz metasurface spectrum prediction
Recently, artificial intelligence has been extensively deployed across various scientific disciplines, optimizing and guiding the progression of experiments through the integration of abundant datasets, whilst continuously probing the vast theoretical space encapsulated within the data.
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Comprehensively evaluating the capability of large language models in multiple tasks is of great importance.
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network
The proposed framework only requires cognitive processing signals recorded under natural reading as inputs, and can be used to detect a wide range of linguistic features with a single cognitive dataset.
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals
Most previous studies integrate cognitive language processing signals (e. g., eye-tracking or EEG data) into neural models of natural language processing (NLP) just by directly concatenating word embeddings with cognitive features, ignoring the gap between the two modalities (i. e., textual vs. cognitive) and noise in cognitive features.
