Visual-to-Language Chart Reasoning With Lower Compute Overhead
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Solution Overview
Problem
Existing visual language reasoning models struggle with complex human-written queries due to high computational costs and limited reasoning capabilities, especially when applied to graphical data like charts and plots, requiring extensive finetuning and significant computational resources.
Innovation Solution
A two-step approach involving a machine-learned visual-to-language conversion model that translates graphical data into text, followed by a language model for reasoning, utilizing pretraining tasks like plot deconstruction and numerical reasoning, and innovative prompting techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end-to-end visual language models are used for graphical data reasoning, then reasoning capabilities are improved, but computational cost and resource requirements increase significantly
Solution Approach 1:
The system segments the visual language processing task into two independent components: a visual encoder that processes graphical depictions and a language model that performs reasoning. This segmentation allows each component to be optimized separately, reducing the overall computational burden while maintaining reasoning capabilities.
Solution Approach 2:
The patent introduces an intermediary representation that bridges visual and language modalities. The visual encoder transforms graphical data into this intermediate form, which then feeds into the language model. This intermediary layer decouples the two complex components, reducing the computational complexity of the end-to-end system.
2Measurement precision
If extensive finetuning is applied to improve model performance on visual language tasks, then reasoning accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The visual encoder is pre-trained on large-scale visual data before being applied to specific visual language tasks. This preliminary training establishes robust visual feature extraction capabilities that can be directly utilized without extensive task-specific finetuning, significantly reducing training time while maintaining high accuracy.
Solution Approach 2:
The system employs a universal language model that has been pre-trained on diverse language tasks and can be applied to various visual language reasoning tasks without task-specific finetuning. This multi-functionality allows the model to generalize across different graphical data types and reasoning tasks, reducing the need for repeated training.
3Measurement precision
If large amounts of task data are used for finetuning to improve performance, then model accuracy is improved, but computational resources and processing power increase
Solution Approach 1:
Instead of training on large amounts of task-specific data, the system uses pre-trained models whose knowledge has been copied from large-scale pre-training corpora. This allows the system to leverage learned patterns and representations without incurring the computational cost of training on task-specific data, maintaining accuracy while reducing processing requirements.
Solution Approach 2:
The system changes the parameters of the pre-trained models through efficient adaptation techniques rather than extensive retraining. This allows the models to adapt to specific visual language tasks with minimal computational resources, achieving high accuracy without requiring large amounts of task data or significant processing power.
4Reliability
If complex humanly-written queries are processed to improve reasoning depth, then answer quality is improved, but processing time and computational overhead increase
Solution Approach 1:
The system replaces complex mechanical reasoning processes with learned patterns from the pre-trained language model. Instead of implementing explicit reasoning mechanisms that would require significant processing time, the model leverages patterns and knowledge acquired during pre-training to rapidly generate high-quality answers to complex queries.
Data Source
AI summary
Provided is a one-shot solution to visual language reasoning. Example systems described herein decompose the challenge of visual language reasoning into two steps: translation of a graphical depiction of data (e.g., a plot or chart) into text; followed by reasoning over the translated text. In particular, example systems described herein can include a machine-learned visual-to-language conversion model that translates a graphical depiction of a dataset to a set of text descriptive of the dataset. The output of visual-to-language conversion model can then be directly used to prompt a language model, (e.g., a pretrained large language model (LLM)), exploiting the few-shot reasoning capabilities of the language model.