Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2501.17785 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916589097451520 |
|---|---|
| author | Shih, Yu-Fei Lin, Zheng-Lin Hsieh, Shu-Kai |
| author_facet | Shih, Yu-Fei Lin, Zheng-Lin Hsieh, Shu-Kai |
| contents | We explore the capabilities of LVLMs and LLMs in deciphering rare scripts not encoded in Unicode. We introduce a novel approach to construct a multimodal dataset of linguistic puzzles involving such scripts, utilizing a tokenization method for language glyphs. Our methods include the Picture Method for LVLMs and the Description Method for LLMs, enabling these models to tackle these challenges. We conduct experiments using prominent models, GPT-4o, Gemini, and Claude 3.5 Sonnet, on linguistic puzzles. Our findings reveal the strengths and limitations of current AI methods in linguistic decipherment, highlighting the impact of Unicode encoding on model performance and the challenges of modeling visual language tokens through descriptions. Our study advances understanding of AI's potential in linguistic decipherment and underscores the need for further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17785 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reasoning Over the Glyphs: Evaluation of LLM's Decipherment of Rare Scripts Shih, Yu-Fei Lin, Zheng-Lin Hsieh, Shu-Kai Computation and Language Machine Learning J.5; I.2.7 We explore the capabilities of LVLMs and LLMs in deciphering rare scripts not encoded in Unicode. We introduce a novel approach to construct a multimodal dataset of linguistic puzzles involving such scripts, utilizing a tokenization method for language glyphs. Our methods include the Picture Method for LVLMs and the Description Method for LLMs, enabling these models to tackle these challenges. We conduct experiments using prominent models, GPT-4o, Gemini, and Claude 3.5 Sonnet, on linguistic puzzles. Our findings reveal the strengths and limitations of current AI methods in linguistic decipherment, highlighting the impact of Unicode encoding on model performance and the challenges of modeling visual language tokens through descriptions. Our study advances understanding of AI's potential in linguistic decipherment and underscores the need for further research. |
| title | Reasoning Over the Glyphs: Evaluation of LLM's Decipherment of Rare Scripts |
| topic | Computation and Language Machine Learning J.5; I.2.7 |
| url | https://arxiv.org/abs/2501.17785 |