Deborah Watty

This work introduces a historical corpus of the Chinese language spanning approximately 3,000 years and proposes a new corpus search system utilizing word embedding techniques and large language models (LLMs). The system adopts a hybrid search method that combines traditional keyword search with vector-based search based on semantic relationships. This approach enables searches for semantically similar words and visualizations of semantic change, which were challenging with conventional corpus search methods. Additionally, based on the collected corpus data, we implemented a feature to visualize changes in word meanings across specific periods and media types. This interface allows for a multifaceted analysis of language evolution, demonstrating a more effective analytical approach than traditional methods.

In this study, we delve into the efficacy of the Tree-of-Thought Prompting technique as a mechanism to address linguistic challenges and augment the reasoning capabilities of expansive language models. Specifically, we scrutinize the reasoning prowess of the Generative Pre-trained Transformer (GPT) model, which has garnered significant attention within the research and practitioner community. Utilizing the Tree-of-Thought Prompting methodology, we assess its utility in enhancing both the precision and response latency of the GPT model, especially for Linguistic Olympiad tasks demanding elevated reasoning competencies. Concurrently, we delineate inherent limitations within this approach and proffer avenues for future research to refine and optimize it. Code repo: https://github.com/chrizeroxtwo/ToT-LinguisticProblem

In automated Braille translation, accommodating linguistic nuances and the rules peculiar to Braille across various languages poses considerable challenges. Mandarin Chinese stands out in this aspect due to its necessity to ascertain the appropriate pronunciation of characters based on context. Although rule-based algorithms have historically dominated this space, recent empirical evidence highlights the efficacy of statistical approaches and the emergent exploration of Large Language Model (LLM)-based techniques. This paper explores the potential advantages of leveraging a prompt-based strategy for the automated translation from Mandarin Chinese to Taiwanese Mandarin Braille. As a methodology, we devised a script capable of ingesting a Chinese sentence and subsequently generating a prompt that comprises the Zhuyin of unequivocal characters and dictionary definitions for those with polysemous readings. Utilizing a set of 103 test sentences, we assessed the precision with which GPT-3.5, GPT-4, and Liblouis (a widely-recognized opensource rule-based Braille translator) ascribed readings to polyphonic characters. Our findings revealed that, notwithstanding certain inconsistencies in the GPT-3.5 outputs, the extended GPT4 model exhibited superior performance compared to Liblouis.