Computational Workflow Tuning Using Trace Feedback Updates
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Solution Overview
Problem
Existing computational workflows that integrate machine learning models require laborious engineering and manual updates to address erroneous behaviors, and existing LLM-based techniques are inefficient when the parameter space is large, as they rely on scalar feedback that does not provide an improvement signal.
Innovation Solution
An end-to-end computational workflow tuning approach using an OPTO framework that extends backpropagation to optimize differentiable and non-differentiable stages, leveraging trace feedback to update parameters in a directed acyclic graph (DAG) structure, incorporating a trace oracle to derive parameter updates from execution traces and output feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updates are used to correct erroneous behaviors in computational workflows, then reliability can be maintained, but productivity and ease of operation deteriorate due to laborious engineering requirements
Solution Approach 1:
The system implements self-service by automatically detecting erroneous behaviors through trace feedback and autonomously generating parameter updates without requiring manual developer intervention. The trace feedback mechanism monitors workflow execution and automatically identifies issues, while the parameter update generation system creates corrections based on the detected errors, enabling the workflow to self-correct and maintain reliability without sacrificing productivity.
Solution Approach 2:
The patent implements feedback through trace feedback that monitors workflow execution and provides information about erroneous behaviors. This feedback loop allows the system to detect when a workflow produces incorrect outputs, generate appropriate parameter updates, and apply these updates to correct the erroneous behaviors, thereby maintaining reliability while improving update efficiency through automation.
2Ease of operation
If LLM-based techniques are used to optimize computational workflows, then ease of operation improves, but productivity deteriorates when the parameter space is large due to reliance on scalar feedback
Solution Approach 1:
The patent introduces trace feedback as an intermediary mechanism between the workflow execution and the parameter update generation. This intermediary provides rich, structured information about the actual execution traces and erroneous behaviors, enabling LLM-based techniques to operate easily while improving productivity by providing actionable feedback that guides efficient parameter updates in large parameter spaces.
Solution Approach 2:
The system enhances feedback by using trace feedback that provides detailed information about workflow execution traces and identified erroneous behaviors. This enriched feedback mechanism enables LLM-based optimization to maintain ease of operation while significantly improving productivity in large parameter spaces by providing specific, actionable guidance rather than relying on vague scalar feedback.
3Adaptability or versatility
If heterogeneous parameters are involved in computational workflows, then adaptability improves, but device complexity increases due to many parameters requiring manual management
Solution Approach 1:
The system achieves self-service by automatically managing heterogeneous parameters through trace feedback-driven parameter updates. Instead of requiring manual management of each parameter, the system autonomously identifies which parameters need updating, generates appropriate updates, and applies them, thereby maintaining adaptability while reducing the complexity of parameter management through automation.
Solution Approach 2:
The patent introduces trace feedback as an intermediary that simplifies the management of heterogeneous parameters. This intermediary provides a unified mechanism for tracking and updating different types of parameters (prompts, orchestration code, ML hyper-parameters) through a consistent feedback loop, thereby maintaining workflow flexibility while reducing parameter management complexity.
Data Source
AI summary
A computing system including one or more processing devices configured to receive context data. The one or more processing devices obtain a workflow graph of a computational workflow. The one or more processing devices process a workflow input at the computational workflow to obtain a workflow output. The one or more processing devices select an adjustable parameter included in the computational workflow. The one or more processing devices compute a trace feedback including an execution trace of the processing of the workflow input starting at a selected workflow node that includes the selected adjustable parameter. The trace feedback further includes an output feedback received in response to the workflow output. The one or more processing devices compute a parameter update to the selected adjustable parameter based at least in part on the context data and the trace feedback and apply the parameter update to the selected adjustable parameter.