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METAREFLECTION: Learning Instructions for Language Agents using Past Reflections
Our results demonstrate a notable improvement, with METARELECTION outperforming GPT-4 by 16. 82% (IAC), 31. 33% (COT), and 15. 42% (REACT), underscoring the potential of METAREFLECTION as a viable method for enhancing the efficiency of LLMs.
TST$^\mathrm{R}$: Target Similarity Tuning Meets the Real World
Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs) to improve performance.
Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs
Inspired by prior work, we generate a collection of self-supervised structural tasks (e. g. navigate to a cell and row; transpose the table) and evaluate the performance differences when using 8 formats.
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