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Causal memory can boost execution accuracy in Text-to-SQL tasks, but its effectiveness is context-dependent and not universally superior to other retrieval methods.
Text encoders face a unique challenge where attempting to compress multiple valid completions into a single latent point leads to catastrophic performance issues.
Existing multilingual encoders can mislead researchers by fragmenting minority languages, but a new corpus and method reveal their true potential.
