Chinese Spell Checking (CSC) is a widely used technology, which plays a vital
role in speech to text (STT) and optical character recognition (OCR). Most of
the existing CSC approaches relying on BERT architecture achieve excellent
performance. However, limited by the scale of the foundation model, BERT-based
method does not work well in few-shot scenarios, showing certain limitations in
practical applications. In this paper, we explore using an in-context learning
method named RS-LLM (Rich Semantic based LLMs) to introduce large language
models (LLMs) as the foundation model. Besides, we study the impact of
introducing various Chinese rich semantic information in our framework. We
found that by introducing a small number of specific Chinese rich semantic
structures, LLMs achieve better performance than the BERT-based model on
few-shot CSC task. Furthermore, we conduct experiments on multiple datasets,
and the experimental results verified the superiority of our proposed
framework.

通过引入小部分具体的中文丰富语义结构，LLMs 在少样本场景下比基于 BERT 模型的方法表现更好，验证了我们提出框架的优越性。

丰富语义知识增强的大型语言模型在少样本汉语拼写检查中的应用

Rich Semantic Knowledge Enhanced Large Language Models for Few-shot  Chinese Spell Checking

There has been an emergence of various models for long-term time series
forecasting. Recent studies have demonstrated that a single linear layer, using
Channel Dependent (CD) or Channel Independent (CI) modeling, can even
outperform a large number of sophisticated models. However, current research
primarily considers CD and CI as two complementary yet mutually exclusive
approaches, unable to harness these two extremes simultaneously. And it is also
a challenging issue that both CD and CI are static strategies that cannot be
determined to be optimal for a specific dataset without extensive experiments.
In this paper, we reconsider whether the current CI strategy is the best
solution for time series forecasting. First, we propose a simple yet effective
strategy called CSC, which stands for $\mathbf{C}$hannel
$\mathbf{S}$elf-$\mathbf{C}$lustering strategy, for linear models. Our Channel
Self-Clustering (CSC) enhances CI strategy's performance improvements while
reducing parameter size, for exmpale by over 10 times on electricity dataset,
and significantly cutting training time. Second, we further propose Channel
Rearrangement (CR), a method for deep models inspired by the self-clustering.
CR attains competitive performance against baselines. Finally, we also discuss
whether it is best to forecast the future values using the historical values of
the same channel as inputs. We hope our findings and methods could inspire new
solutions beyond CD/CI.

本研究提出了一种名为 CSC 的简单而有效的策略，用于线性模型，它提高了 CI 策略的性能并减小了参数大小，同时大大缩短了训练时间，并且还提出了一种基于自聚类的深度模型方法 CR，以及对于使用相同频道的历史值作为输入来预测未来值的讨论。研究结果希望能够启发出超越 CD/CI 的新解决方案。

时间序列预测的通道独立策略是否最优？

Is Channel Independent strategy optimal for Time Series Forecasting?

Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling
errors. Recent researches start from the pretrained knowledge of language
models and take multimodal information into CSC models to improve the
performance. However, they overlook the rich knowledge in the dictionary, the
reference book where one can learn how one character should be pronounced,
written, and used. In this paper, we propose the LEAD framework, which renders
the CSC model to learn heterogeneous knowledge from the dictionary in terms of
phonetics, vision, and meaning. LEAD first constructs positive and negative
samples according to the knowledge of character phonetics, glyphs, and
definitions in the dictionary. Then a unified contrastive learning-based
training scheme is employed to refine the representations of the CSC models.
Extensive experiments and detailed analyses on the SIGHAN benchmark datasets
demonstrate the effectiveness of our proposed methods.

本文提出了一种 LEAD 框架，该框架允许 CSC 模型从字典的语音、视觉和含义方面学习异构知识，并通过基于对比学习的训练方案来提高 CSC 模型的表示能力。实验证明该方法有效。