11 papers
Revisiting the Shape Convention of Transformer Language Models
Feng-Ting Liao, Meng-Hsi Chen, Guan-Ting Yi +1
Dense Transformer language models have largely adhered to one consistent architectural shape: each layer consists of an attention module followed by a feed-forward network (FFN) wi…
TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling
Liang-Hsuan Tseng, Yi-Chang Chen, Kuan-Yi Lee +2
Recent efforts target spoken language models (SLMs) that not only listen but also speak for more natural human-LLM interaction. Joint speech-text modeling is a promising direction…
Rethinking the shape convention of an MLP
Meng-Hsi Chen, Yu-Ang Lee, Feng-Ting Liao +1
Multi-layer perceptrons (MLPs) conventionally follow a narrow-wide-narrow design where skip connections operate at the input/output dimensions while processing occurs in expanded h…
Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Robust and Instruction-Aware ASR and OCR
Chan-Jan Hsu, Yi-Chang Chen, Feng-Ting Liao +4
We propose "Generative Fusion Decoding" (GFD), a novel shallow fusion framework designed to integrate large language models (LLMs) into cross-modal text recognition systems for aut…
Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds
Aya Kayal, Sattar Vakili, Laura Toni +2
Bayesian optimization (BO) with preference-based feedback has recently garnered significant attention due to its emerging applications. We refer to this problem as Bayesian Optimiz…
Towards a Foundation Model for Communication Systems
Davide Buffelli, Sowmen Das, Yu-Wei Lin +5
Artificial Intelligence (AI) has demonstrated unprecedented performance across various domains, and its application to communication systems is an active area of research. While cu…