Zobrazeno 1 - 10
of 192
pro vyhledávání: '"Chen, Lizhong"'
Ranking passages by prompting a large language model (LLM) can achieve promising performance in modern information retrieval (IR) systems. A common approach is to sort the ranking list by prompting LLMs for pairwise comparison. However, sorting-based
Externí odkaz:
http://arxiv.org/abs/2406.00231
A promising approach to preserving model performance in linearized transformers is to employ position-based re-weighting functions. However, state-of-the-art re-weighting functions rely heavily on target sequence lengths, making it difficult or impos
Externí odkaz:
http://arxiv.org/abs/2405.13046
Large language models (LLMs) have achieved state-of-the-art performance in various language processing tasks, motivating their adoption in simultaneous translation. Current fine-tuning methods to adapt LLMs for simultaneous translation focus on promp
Externí odkaz:
http://arxiv.org/abs/2405.10443
Tensors play a vital role in machine learning (ML) and often exhibit properties best explored while maintaining high-order. Efficiently performing ML computations requires taking advantage of sparsity, but generalized hardware support is challenging.
Externí odkaz:
http://arxiv.org/abs/2404.16317
Large language models (LLMs) with billions of parameters and pretrained on massive amounts of data are now capable of near or better than state-of-the-art performance in a variety of downstream natural language processing tasks. Neural machine transl
Externí odkaz:
http://arxiv.org/abs/2312.04691
Autor:
Raffel, Matthew, Chen, Lizhong
Simultaneous speech translation is an essential communication task difficult for humans whereby a translation is generated concurrently with oncoming speech inputs. For such a streaming task, transformers using block processing to break an input sequ
Externí odkaz:
http://arxiv.org/abs/2307.01381
Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation
Transformer models using segment-based processing have been an effective architecture for simultaneous speech translation. However, such models create a context mismatch between training and inference environments, hindering potential translation acc
Externí odkaz:
http://arxiv.org/abs/2307.01377
Compactness in deep learning can be critical to a model's viability in low-resource applications, and a common approach to extreme model compression is quantization. We consider Iterative Product Quantization (iPQ) with Quant-Noise to be state-of-the
Externí odkaz:
http://arxiv.org/abs/2306.14031
Autor:
Agostinelli, Victor, Chen, Lizhong
Various natural language processing (NLP) tasks necessitate models that are efficient and small based on their ultimate application at the edge or in other resource-constrained environments. While prior research has reduced the size of these models,
Externí odkaz:
http://arxiv.org/abs/2304.08453
Autor:
Penney, Drew, Li, Bin, Chen, Lizhong, Sydir, Jaroslaw J., Drewek-Ossowicka, Anna, Illikkal, Ramesh, Tai, Charlie, Iyer, Ravi, Herdrich, Andrew
Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership. Effective resource sharing, however, remains an open chall
Externí odkaz:
http://arxiv.org/abs/2304.04797