A prompting framework that recursively decomposes reasoning tasks and self-scores candidate thoughts is reported to improve LLM accuracy on math and letter-concatenation benchmarks, though the headline improvement is overstated relative to the presented baselines.
NL-EDIT: Correcting semantic parse errors through natural language interaction
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abstract
We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.
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Recursive Decomposition of Logical Thoughts: Framework for Superior Reasoning and Knowledge Propagation in Large Language Models
A prompting framework that recursively decomposes reasoning tasks and self-scores candidate thoughts is reported to improve LLM accuracy on math and letter-concatenation benchmarks, though the headline improvement is overstated relative to the presented baselines.