Minimal collectives of three LLM agents develop spontaneous cooperation, storage strategies, and complex evolving cultural artifacts via interaction with a decaying shared text store and evolutionary pressure.
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A Structured Self-attentive Sentence Embedding
12 Pith papers cite this work. Polarity classification is still indexing.
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abstract
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification, and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks.
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