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 Deep Learning



SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models

Neural Information Processing Systems

From an optimization perspective, our SLED framework leverages the latent knowledge embedded within the LLM by contrasting the output logits from the final layer with those from early layers.




PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression

Neural Information Processing Systems

There has been significant interest in "extreme" compression of large language models (LLMs), i.e., to 1-2 bits per parameter, which allows such models to be executed efficiently on resource-constrained devices.