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Found 4 papers, 2 papers with code
Everyone Deserves A Reward: Learning Customized Human Preferences
Besides, from the perspective of data efficiency, we propose a three-stage customized RM learning scheme, then empirically verify its effectiveness on both general preference datasets and our DSP set.
Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers
Although dominant in natural language processing, transformer-based models remain challenged by the task of long-sequence processing, because the computational cost of self-attention operations in transformers swells quadratically with the input sequence length.
Automatic lobe segmentation using attentive cross entropy and end-to-end fissure generation
Therefore, we propose a new automatic lung lobe segmentation framework, in which we urge the model to pay attention to the area around the pulmonary fissure during the training process, which is realized by a task-specific loss function.
Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive Summarization
Most Transformer based abstractive summarization systems have a severe mismatch between training and inference, i. e., exposure bias.
