Papers by Matthew Raffel
Implicit Memory Transformer for Computationally Efficient Simultaneous Speech Translation (2023.findings-acl)
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| Challenge: | Simultaneous speech translation is an essential communication task difficult for humans whereby a translation is generated concurrently with oncoming speech inputs. |
| Approach: | They propose a transformer that implicitly retains memory through a new left context method, removing the need to explicitly represent memory with memory banks. |
| Outcome: | The proposed method provides a substantial speedup on the encoder forward pass with nearly identical translation quality when compared with the state-of-the-art approach that uses left context and memory banks. |
Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models (2024.acl-long)
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| Challenge: | Modern large language models (LLMs) contain billions of parameters and can perform a variety of downstream tasks. |
| Approach: | They propose an open-source framework for fine-tuning large language models (LLMs) they address key challenges facing LLMs fine- tuned for simultaneous translation . |
| Outcome: | The proposed framework validates classical SimulMT concepts and practices in the context of LLMs and explores adapting LLM fine-tuned for NMT to the task of Simul-LLM. |
Simultaneous Masking, Not Prompting Optimization: A Paradigm Shift in Fine-tuning LLMs for Simultaneous Translation (2024.emnlp-main)
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| Challenge: | Current fine-tuning methods to adapt LLMs for simultaneous translation suffer from several issues, such as unnecessarily expanded training sets, increased prompt sizes, or restriction to a single decision policy. |
| Approach: | They propose a new paradigm for fine-tuning large language models for simultaneous translation using an attention mask approach. |
| Outcome: | The proposed model improves translation quality compared to state-of-the-art models on five language pairs while reducing the computational cost. |