Papers by Ethan Chau

2 papers
Automatic Pair Construction for Contrastive Post-training (2024.findings-naacl)

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Challenge: Large language models (LLMs) have unprecedented proficiency in a wide array of tasks.
Approach: They propose a way to construct contrastive data using preference pairs from multiple models of varying strengths using SLiC and DPO.
Outcome: The proposed method outperforms existing models like Orca in the comparison of SLiC and DPO with SFT baselines.
Dodo: Dynamic Contextual Compression for Decoder-only LMs (2024.acl-long)

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Challenge: Existing approaches to NLP are sparsifying attention patterns or approximating the attention computation with kernel methods.
Approach: They propose a method for dynamic contextual compression for decoder-only LMs.
Outcome: The proposed method reduces the cost of self-attention to a fraction of typical time and space.

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