Search papers, labs, and topics across Lattice.
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Selection bias in Positive-Unlabeled learning can be mitigated, leading to significant performance gains in real-world classification tasks.
MATCH reveals that integrating in-context retrieval can dramatically boost the performance of sparse attention models without sacrificing efficiency.
Semantic acceptance rates can be misleading, with up to 44.2% of models failing to prevent observable harm even when they pass initial checks.
Skill rewriting can reduce operational costs by over 14% without sacrificing performance, challenging the notion that shorter skills are always better.
Diffusion Language Models are being held back by auto-regressive thinking, and unlocking their true potential requires a complete paradigm shift.
