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Token-level credit assignment can drastically improve the effectiveness of generative document retrieval, leading to better alignment between generation and relevance.
Achieving state-of-the-art multilingual reranking without costly relevance annotations, Querit-Reranker redefines efficiency in model adaptation.
A 7B parameter model, optimized with multi-task learning and RL, rivals the timeline summarization performance of a 671B parameter model, proving that task-specific fine-tuning can dramatically shrink model size without sacrificing quality.
Multimodal embeddings get a serious upgrade with CoCoA, a new pre-training method that forces models to compress all input information into a single token for reconstruction, leading to substantial quality gains.
