Compressing LLM text embeddings with an autoencoder to about 8 dimensions improves stock return prediction, but this benefit disappears on high-signal tasks, and sentiment features seem to work mainly because of compression.
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When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks
Compressing LLM text embeddings with an autoencoder to about 8 dimensions improves stock return prediction, but this benefit disappears on high-signal tasks, and sentiment features seem to work mainly because of compression.