Controlled Decoding With Segment Scoring for Early Termination
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Solution Overview
Problem
Existing machine-learned models often require significant computational resources and data to align their output with desired characteristics, and open-loop generation methods fail to detect suboptimal candidate quality mid-generation, leading to inefficient and energy-intensive operations.
Innovation Solution
Implement a closed-loop evaluation mechanism using machine-learned scoring models to segment candidate outputs into semantic units, applying filters to select high-quality segments and terminate suboptimal generations early, thereby improving energy efficiency and output quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If open-loop generation methods are used to generate candidate outputs, then the generation process is simple to implement, but the system fails to detect suboptimal candidate quality mid-generation, leading to inefficient and energy-intensive operations
Solution Approach 1:
The patent introduces a closed-loop evaluation mechanism where scoring models continuously evaluate candidate segments during generation. The system uses feedback from segment-level and response-level scoring to dynamically adjust generation, terminating suboptimal candidates early while maintaining high quality outputs, thereby reducing energy consumption without compromising implementation feasibility
Solution Approach 2:
The patent applies preliminary filtering by evaluating candidate segments at intermediate stages rather than waiting for complete generation. By performing segment-level scoring during the generation process, the system can identify and terminate suboptimal candidates early, preventing wasted computational energy on poor-quality outputs
2Manufacturing precision
If the system generates and evaluates all candidate segments to ensure high quality output, then output quality is maintained, but computational resources and time are significantly consumed
Solution Approach 1:
The patent divides the candidate output into multiple segments and applies hierarchical evaluation: segment-level scoring assesses local quality while response-level scoring evaluates overall quality. This segmentation allows early termination of suboptimal candidates at the segment level, maintaining output quality while improving generation efficiency by avoiding full evaluation of poor candidates
Solution Approach 2:
The patent implements partial evaluation by assessing only the necessary portions of candidate segments. Using segment-level scoring, the system evaluates candidates incrementally and terminates evaluation early for suboptimal segments, performing less than full evaluation while still ensuring high-quality outputs through selective filtering
3Adaptability or versatility
If continuous generation of candidate segments is performed without mid-process termination, then all possible candidates are explored, but suboptimal generations consume unnecessary computational resources
Solution Approach 1:
The patent implements closed-loop feedback where scoring models continuously evaluate generated segments and provide signals for early termination. This feedback mechanism allows the system to adaptively terminate suboptimal generations mid-process while maintaining exploration of viable candidates, reducing generation time without sacrificing candidate diversity
Solution Approach 2:
The patent introduces dynamic control over the generation process by allowing mid-process termination based on real-time evaluation. The system adaptively adjusts generation depth for different candidates, terminating promising candidates early while continuing exploration of potentially high-quality candidates, thereby optimizing both time efficiency and candidate exploration
Data Source
AI summary
An example method includes inputting a first segment of a sequence into a machine-learned sequence processing model, wherein the first segment comprises data associated with a sequence generation request. The example method includes generating, in parallel, a plurality of candidate second segments. The example method includes generating a plurality of scores respectively for the plurality of candidate second segments using a segment quality model to generate a first component score and a response quality model to generate a second component score. The example method includes selecting, based on the plurality of scores, a second segment based on the plurality of candidate second segments. The example method includes processing the first segment and the selected second segment using the machine-learned sequence processing model to generate a third segment. The example method includes returning the selected second segment and the third segment in response to the sequence generation request.