Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Fathullah, Yassir"'
There has been increasing interest in building multilingual foundation models for NLP and speech research. This paper examines how to expand the speech translation capability of these models with restricted data. Whisper, a speech foundation model wi
Externí odkaz:
http://arxiv.org/abs/2407.01130
LLM-as-a-judge approaches are a practical and effective way of assessing a range of text tasks. However, when using pairwise comparisons to rank a set of candidates, the computational cost scales quadratically with the number of candidates, which has
Externí odkaz:
http://arxiv.org/abs/2405.05894
Autor:
Fathullah, Yassir, Gales, Mark J. F.
Encoder-decoder foundation models have displayed state-of-the-art performance on a range of autoregressive sequence tasks. This paper proposes a simple and lightweight modification to such systems to control the behaviour according to a specific attr
Externí odkaz:
http://arxiv.org/abs/2405.01601
Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities and versatility in NLP tasks, however they sometimes fail to maintain crucial invariances for specific tasks. One example is permutation sensitivity, where LLMs' outputs
Externí odkaz:
http://arxiv.org/abs/2403.13590
Autor:
Fathullah, Yassir, Wu, Chunyang, Lakomkin, Egor, Li, Ke, Jia, Junteng, Shangguan, Yuan, Mahadeokar, Jay, Kalinli, Ozlem, Fuegen, Christian, Seltzer, Mike
In this work, we extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities while maintaining the wide range of original LLM capabilities, without using any carefully curated paired data. The
Externí odkaz:
http://arxiv.org/abs/2311.06753
Autor:
Lakomkin, Egor, Wu, Chunyang, Fathullah, Yassir, Kalinli, Ozlem, Seltzer, Michael L., Fuegen, Christian
In recent years, Large Language Models (LLMs) have garnered significant attention from the research community due to their exceptional performance and generalization capabilities. In this paper, we introduce a novel method for contextualizing speech
Externí odkaz:
http://arxiv.org/abs/2309.10917
Autor:
Shangguan, Yuan, Yang, Haichuan, Li, Danni, Wu, Chunyang, Fathullah, Yassir, Wang, Dilin, Dalmia, Ayushi, Krishnamoorthi, Raghuraman, Kalinli, Ozlem, Jia, Junteng, Mahadeokar, Jay, Lei, Xin, Seltzer, Mike, Chandra, Vikas
Automatic Speech Recognition (ASR) models need to be optimized for specific hardware before they can be deployed on devices. This can be done by tuning the model's hyperparameters or exploring variations in its architecture. Re-training and re-valida
Externí odkaz:
http://arxiv.org/abs/2309.01947
Autor:
Fathullah, Yassir, Wu, Chunyang, Lakomkin, Egor, Jia, Junteng, Shangguan, Yuan, Li, Ke, Guo, Jinxi, Xiong, Wenhan, Mahadeokar, Jay, Kalinli, Ozlem, Fuegen, Christian, Seltzer, Mike
Large language models have proven themselves highly flexible, able to solve a wide range of generative tasks, such as abstractive summarization and open-ended question answering. In this paper we extend the capabilities of LLMs by directly attaching
Externí odkaz:
http://arxiv.org/abs/2307.11795
In this paper, we consider the challenge of summarizing patients' medical progress notes in a limited data setting. For the Problem List Summarization (shared task 1A) at the BioNLP Workshop 2023, we demonstrate that Clinical-T5 fine-tuned to 765 med
Externí odkaz:
http://arxiv.org/abs/2306.05317
Autor:
Fathullah, Yassir, Wu, Chunyang, Shangguan, Yuan, Jia, Junteng, Xiong, Wenhan, Mahadeokar, Jay, Liu, Chunxi, Shi, Yangyang, Kalinli, Ozlem, Seltzer, Mike, Gales, Mark J. F.
State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks, rivalling and outperforming many attention-based approaches. In this paper, we propose a multi-head state space (MH-SSM) architectur
Externí odkaz:
http://arxiv.org/abs/2305.12498