6 papers
Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders
Benjamin Rozonoyer, Chong You, Michael Boratko +5
The success of Large Language Models (LLMs) has motivated a shift toward generative approaches to retrieval and ranking, aiming to supersede classical Dual Encoders (DEs) and Cross…
Spark Transformer: Reactivating Sparsity in FFN and Attention
Chong You, Kan Wu, Zhipeng Jia +16
The discovery of the lazy neuron phenomenon in trained Transformers, where the vast majority of neurons in their feed-forward networks (FFN) are inactive for each token, has spurre…
Scalable In-context Ranking with Generative Models
Nilesh Gupta, Chong You, Srinadh Bhojanapalli +3
In-context Ranking (ICR) is an emerging paradigm for Information Retrieval (IR), which leverages contextual understanding of LLMs by directly incorporating the task description, ca…
Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count
Hanseul Cho, Jaeyoung Cha, Srinadh Bhojanapalli +1
Transformers often struggle with length generalization, meaning they fail to generalize to sequences longer than those encountered during training. While arithmetic tasks are commo…
Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure
Hanseul Cho, Jaeyoung Cha, Pranjal Awasthi +3
Even for simple arithmetic tasks like integer addition, it is challenging for Transformers to generalize to longer sequences than those encountered during training. To tackle this…
Mimetic Initialization Helps State Space Models Learn to Recall
Asher Trockman, Hrayr Harutyunyan, J. Zico Kolter +2
Recent work has shown that state space models such as Mamba are significantly worse than Transformers on recall-based tasks due to the fact that their state size is constant with r…