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beiya dai

2 accepted papers

2026

Context-level Language Modeling by Learning Predictive Context Embeddings

ICML 2026poster

We propose ContextLM, a framework that implicitly learns multi-token prediction by augmenting standard pretraining with an intrinsic next-context prediction objective. ContextLM builds a language model on top of context embeddings that span multiple tokens, enabling better next-token prediction by p…

Cited by 0SourceScholar
2025

Efficient Long Document Ranking via Adaptive Token Pruning with Query-Document Alignment

ICASSP 2025accepted

Transformer-based models have achieved great success in document ranking, yet they suffer from substantial computational costs due to the quadratic complexity of attention, particularly for Long Document Ranking (LDR). Token pruning is a promising approach to reducing computational costs, while exis…

Cited by 0SourceScholar