Language Model Intermediate Text Analysis for Contextual Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
State-of-the-art neural language models for contextual text generation suffer from limited factual knowledge, often hallucinating incorrect facts or supplying outdated information, and lack interpretability, leading to inefficiencies and unnecessary resource usage.
Innovation Solution
A machine-learned language model that generates intermediate textual analysis before output, leveraging structural tools to access additional information and improve interpretability, allowing it to generate up-to-date and domain-specific responses without re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If neural language models directly generate output from input, then generation speed is improved, but interpretability deteriorates
Solution Approach 1:
The patent segments the text generation process into distinct stages: contextual text input, intermediate textual analysis generation, and final output generation. This segmentation allows the model to produce interpretable intermediate analysis that explains the reasoning process while maintaining generation efficiency through the structured pipeline.
Solution Approach 2:
The patent introduces intermediate textual analysis as a mediator between the input contextual text and the final output. This intermediate layer provides interpretability by explicitly showing the model's reasoning process, while the overall system maintains speed through optimized generation at each stage.
2Device complexity
If model knowledge is constrained to training data, then model complexity is reduced, but knowledge up-to-date capability deteriorates
Solution Approach 1:
The patent performs preliminary actions by generating intermediate textual analysis that identifies factual information needs before final output generation. This allows the system to query external knowledge sources selectively for specific facts while keeping the core model relatively simple, ensuring up-to-date knowledge without requiring constant model retraining.
Solution Approach 2:
The intermediate textual analysis acts as an intermediary that bridges the simple core model and external knowledge sources. It identifies what information is needed, queries external sources for up-to-date facts, and integrates this information into the final output, maintaining both model simplicity and knowledge currency.
3Difficulty of detecting and measuring
If intermediate textual analysis is generated, then interpretability is improved, but computational overhead increases
Solution Approach 1:
The patent segments the generation process to produce intermediate textual analysis only when needed for interpretability, rather than always generating full intermediate steps. This selective segmentation provides interpretability where required while reducing computational overhead in cases where it is less critical.
Solution Approach 2:
The patent implements partial action by generating intermediate textual analysis at appropriate granularity levels - providing sufficient detail for interpretability without generating excessive intermediate content. This balances the need for explanation with computational efficiency.
Data Source
AI summary
The present disclosure is directed to systems and methods that include and/or leverage one or more machine-learned language models that generate intermediate textual analysis (e.g., including usage of structural tools such as APIs) in service of contextual text generation. For example, a computing system can obtain a contextual text string that includes one or more contextual text tokens. The computing system can process the contextual text string with the machine-learned language model to generate one or more intermediate text strings that include one or more intermediate text tokens. The computing system can process the one or more intermediate text strings with the machine-learned language model to generate an output text string comprising one or more output text tokens. The one or more intermediate text strings can include textual analysis of the contextual text string that supports the output text string.