Programmer-Interpreter Framework for LLM Post-Editing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large pre-trained language models face challenges in fine-tuning for specific tasks due to limited computational resources or inaccessible parameters, leading to suboptimal performance in low-resource sequence generation tasks like machine translation and logical form-to-text translation, where minimal task-specific data is available during pre-training.
Innovation Solution
A programmer-interpreter framework is introduced, where a smaller language model fine-tuned on task-specific data provides edit instructions to the large language model, enabling iterative refinement and domain generalization, preserving the model's ability to generalize across domains without the need for additional fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a smaller model is used for post-editing to improve text quality, then text quality improves, but domain generalization ability deteriorates
Solution Approach 1:
The system segments the post-editing function into two distinct components: a programmer that generates edit instructions and an interpreter that executes them. The programmer (smaller model) focuses on generating precise edit instructions for text quality, while the interpreter (larger model) handles domain generalization and executes the edits, thereby resolving the contradiction between text quality and domain generalization ability.
Solution Approach 2:
The interpreter acts as an intermediary between the programmer and the final output. The programmer generates edit instructions based on task-specific knowledge, and the interpreter translates these instructions into actual text edits while maintaining domain generalization capabilities, thus mediating between the conflicting requirements of text quality and adaptability.
2Adaptability or versatility
If a large language model is used directly for text generation, then domain generalization is maintained, but text quality in low-resource tasks deteriorates
Solution Approach 1:
The system divides the text generation process into two stages: an initial generation stage using the large language model to maintain domain generalization, and a post-editing stage using the programmer-interpreter framework to improve text quality. This segmentation allows each component to specialize in its strength without compromising the other.
Solution Approach 2:
The system implements a feedback mechanism where the programmer analyzes the initial output from the large language model and generates edit instructions to improve text quality. This feedback loop allows the system to maintain domain generalization while systematically improving text quality through iterative refinement.
3Manufacturing precision
If fine-tuning is performed on large language models to improve task-specific performance, then text quality improves, but computational cost and accessibility deteriorate
Solution Approach 1:
Instead of fine-tuning the entire large language model (which is computationally expensive), the system uses a smaller, more economical programmer model that generates edit instructions. This approach is computationally cheaper and more accessible, while still achieving improved text quality through the post-editing process.
Solution Approach 2:
The programmer acts as an intermediary that bridges the gap between task-specific requirements and the large language model. Rather than modifying the large model itself, the programmer generates targeted edit instructions that improve text quality without requiring expensive fine-tuning of the entire system.
Data Source
AI summary
A system and method of programmer-interpreter approach for large language model post-editing is described. A method includes receiving, by a generator in a text generation system, an input, translating, by the generator, the input into an initial output text using at least a set of examples retrieved by a function in the generator when performing in-context learning, iteratively refining, by a programmer and an interpreter in the text generation system, the initial output text or intermediate output text, wherein the programmer encodes domain task-specific knowledge and the interpreter facilitates domain generalization, iteratively improving a quality of the initial output text or the intermediate output text in low-resource cross-domain text generation tasks by exploiting encoding of domain task-specific knowledge by the interpreter and facilitation of domain generalization by the interpreter, and outputting, by the text generation system, output text based on the initial output text and the intermediate output text.