Chaining Language Models for Transparent Human-AI Interaction
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Solution Overview
Problem
Current large language models (LLMs) face challenges in handling complex tasks that require multi-step reasoning, are opaque, and difficult to debug or interpret, leading to lower quality outputs and limited user control.
Innovation Solution
The proposed solution involves chaining multiple instantiations of machine-learned language models together, where each model's output serves as the input for the next, allowing for modular construction and editing of model chains through a user-friendly interface. This approach enhances transparency, controllability, and collaboration in human-AI interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single large language model is used to handle complex tasks, then the model can process tasks with a single run, but the quality of outputs decreases for multi-step reasoning tasks and the model becomes opaque and difficult to debug
Solution Approach 1:
The patent segments a complex task into multiple sub-tasks and processes them through a sequence of model instantiations (chain of thought). Each sub-task is handled by a separate model instantiation that receives the previous output as input, enabling step-by-step reasoning while maintaining overall task coherence. This segmentation improves output quality for complex tasks without sacrificing processing efficiency.
2Device complexity
If a single large language model is used, then the system structure remains simple, but the system becomes opaque and hard to interpret or debug
Solution Approach 1:
By segmenting the model into multiple instantiations that process sub-tasks sequentially, the patent creates an interpretable chain of reasoning steps. Each instantiation's input and output can be individually examined, making the overall system transparent and debuggable while maintaining relatively simple structural organization.
Solution Approach 2:
The patent introduces intermediate outputs between model instantiations that serve as interpretable representations of the reasoning process. These intermediates act as mediators that can be examined, edited, and understood, providing transparency into how the model arrives at final conclusions without complicating the overall system architecture.
3Extent of automation
If a single large language model is used, then the model can operate autonomously, but user control and feedback mechanisms are limited
Solution Approach 1:
The segmented chain of model instantiations allows users to intervene at multiple points in the reasoning process. Users can examine intermediate outputs, provide feedback on specific sub-tasks, and control the flow of information through the chain, enhancing ease of operation while preserving overall task automation.
Solution Approach 2:
The patent implements feedback mechanisms where intermediate outputs from each model instantiation can be reviewed and modified before being passed to the next instantiation. This allows continuous user feedback throughout the processing chain, improving user control without sacrificing the automated nature of the task execution.
Data Source
AI summary
The present disclosure provides to transparent and controllable human-AI interaction via chaining of machine-learned language models. In particular, although existing language models (e.g., so-called “large language models” (LLMs)) have demonstrated impressive potential on simple tasks, their breadth of scope, lack of transparency, and insufficient controllability can make them less effective when assisting humans on more complex tasks. In response, the present disclosure introduces the concept of chaining instantiations of machine-learned language models (e.g., LLMs) together, where the output of one instantiation becomes the input for the next, and so on, thus aggregating the gains per step.