Reasoning Over the Glyphs: Evaluation of LLM's Decipherment of Rare Scripts
- URL: http://arxiv.org/abs/2501.17785v1
- Date: Wed, 29 Jan 2025 17:24:19 GMT
- Title: Reasoning Over the Glyphs: Evaluation of LLM's Decipherment of Rare Scripts
- Authors: Yu-Fei Shih, Zheng-Lin Lin, Shu-Kai Hsieh,
- Abstract summary: We introduce a novel approach to construct a multimodal dataset of linguistic puzzles involving rare scripts.
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.
- Score: 0.6144680854063939
- License:
- Abstract: 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.
Related papers
- Prompt and circumstance: A word-by-word LLM prompting approach to interlinear glossing for low-resource languages [6.4977738682502295]
We investigate the effectiveness of a retrieval-based LLM prompting approach to glossing, applied to the seven languages from the SIGMORPHON 2023 shared task.
Our system beats the BERT-based shared task baseline for every language in the morpheme-level score category.
In a case study on Tsez, we ask the LLM to automatically create and follow linguistic instructions, reducing errors on a confusing grammatical feature.
arXiv Detail & Related papers (2025-02-13T21:23:16Z) - LatentQA: Teaching LLMs to Decode Activations Into Natural Language [72.87064562349742]
We introduce LatentQA, the task of answering open-ended questions about model activations in natural language.
We propose Latent Interpretation Tuning (LIT), which finetunes a decoder LLM on a dataset of activations and associated question-answer pairs.
Our decoder also specifies a differentiable loss that we use to control models, such as debiasing models on stereotyped sentences and controlling the sentiment of generations.
arXiv Detail & Related papers (2024-12-11T18:59:33Z) - Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-Language Models [18.87130615326443]
Vision-language models (VLMs) serve as foundation models for image captioning and text-to-image generation.
Recent studies have highlighted limitations in VLM text encoders, particularly in areas like compositionality and semantic understanding.
arXiv Detail & Related papers (2024-12-11T05:37:04Z) - Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation [6.688680877428467]
We propose a novel gloss-free Multimodal Sign Language Translation framework.
We generate detailed textual descriptions of sign language components using multimodal large language models.
Our approach achieves state-of-the-art performance on benchmark datasets PHOENIX14T and CSL-Daily.
arXiv Detail & Related papers (2024-11-25T09:01:41Z) - Negation Blindness in Large Language Models: Unveiling the NO Syndrome in Image Generation [63.064204206220936]
Foundational Large Language Models (LLMs) have changed the way we perceive technology.
They have been shown to excel in tasks ranging from poem writing to coding to essay generation and puzzle solving.
With the incorporation of image generation capability, they have become more comprehensive and versatile AI tools.
Currently identified flaws include hallucination, biases, and bypassing restricted commands to generate harmful content.
arXiv Detail & Related papers (2024-08-27T14:40:16Z) - Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer [5.355430735475281]
This paper investigates the complexities of multilingual prompt-based code generation.
Our evaluations reveal significant disparities in code quality for non-English prompts.
We propose a zero-shot cross-lingual approach using a neural projection technique.
arXiv Detail & Related papers (2024-08-19T05:11:46Z) - Large Language Models are Interpretable Learners [53.56735770834617]
In this paper, we show a combination of Large Language Models (LLMs) and symbolic programs can bridge the gap between expressiveness and interpretability.
The pretrained LLM with natural language prompts provides a massive set of interpretable modules that can transform raw input into natural language concepts.
As the knowledge learned by LSP is a combination of natural language descriptions and symbolic rules, it is easily transferable to humans (interpretable) and other LLMs.
arXiv Detail & Related papers (2024-06-25T02:18:15Z) - Beyond Text: Frozen Large Language Models in Visual Signal Comprehension [34.398976855955404]
Vision-to-Language Tokenizer, abbreviated as V2T Tokenizer, transforms an image into a foreign language'' with the combined aid of an encoder-decoder, the LLM vocabulary, and a CLIP model.
We undertake rigorous experiments to validate our method, encompassing understanding tasks like image recognition, image captioning, and visual question answering, as well as image denoising tasks like inpainting, outpainting, deblurring, and shift restoration.
arXiv Detail & Related papers (2024-03-12T17:59:51Z) - Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora.
We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs.
Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
arXiv Detail & Related papers (2024-02-26T09:36:05Z) - If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code
Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
Large language models (LLMs) are trained on a combination of natural language and formal language (code)
Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
arXiv Detail & Related papers (2024-01-01T16:51:20Z) - Let Models Speak Ciphers: Multiagent Debate through Embeddings [84.20336971784495]
We introduce CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue.
By deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights.
This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs.
arXiv Detail & Related papers (2023-10-10T03:06:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.