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Found 10 papers, 1 papers with code
Does Correction Remain A Problem For Large Language Models?
As large language models, such as GPT, continue to advance the capabilities of natural language processing (NLP), the question arises: does the problem of correction still persist?
Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System
Dialogue data in real scenarios tend to be sparsely available, rendering data-starved end-to-end dialogue systems trained inadequately.
Evaluating the Performance of Large Language Models on GAOKAO Benchmark
Large Language Models(LLMs) have demonstrated remarkable performance across various natural language processing tasks; however, how to comprehensively and accurately assess their performance becomes an urgent issue to be addressed.
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection
To address this issue, we propose a two-branch approach that consists of a local branch for detecting structural anomalies and a global branch for detecting logical anomalies.
Ranked #16 on
Anomaly Detection
on MVTec LOCO AD
Investigating Glyph Phonetic Information for Chinese Spell Checking: What Works and What's Next
While pre-trained Chinese language models have demonstrated impressive performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task remains a challenge.
SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
To adapt BERT to the CSC task, we propose a token-level self-distillation contrastive learning method.
Extracting dispersion curves from ambient noise correlations using deep learning
We present a machine-learning approach to classifying the phases of surface wave dispersion curves.
Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection
Semantic parsing, i. e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language.
