Deterministic orchestration matches LLM-controlled methods in COBOL-to-Python translation accuracy but improves worst-case robustness, reduces run-to-run variability, and cuts token consumption by up to 3.5 times.
Measuring the impact of programming language distribution
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
roles
baseline 1polarities
baseline 1representative citing papers
Repeating training data up to 4 epochs yields negligible loss increase versus unique data for fixed compute, and a new scaling law accounts for the decaying value of repeated tokens and excess parameters.
Self-Debugging teaches LLMs to identify and fix their own code errors through rubber-duck-style natural language explanations and execution feedback, delivering 2-12% gains over baselines on Spider, TransCoder, and MBPP.
citing papers explorer
-
Deterministic vs. LLM-Controlled Orchestration for COBOL-to-Python Modernization
Deterministic orchestration matches LLM-controlled methods in COBOL-to-Python translation accuracy but improves worst-case robustness, reduces run-to-run variability, and cuts token consumption by up to 3.5 times.
-
Scaling Data-Constrained Language Models
Repeating training data up to 4 epochs yields negligible loss increase versus unique data for fixed compute, and a new scaling law accounts for the decaying value of repeated tokens and excess parameters.
-
Teaching Large Language Models to Self-Debug
Self-Debugging teaches LLMs to identify and fix their own code errors through rubber-duck-style natural language explanations and execution feedback, delivering 2-12% gains over baselines on Spider, TransCoder, and MBPP.