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20242026
most citedOn The Landscape of Spoken Language Models: A Comprehensive Survey

3 citations · 3 across the 3 of their papers we have counts for

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cs.CL2026

Knowing What to Stress: A Discourse-Conditioned Text-to-Speech Benchmark

Arnon Turetzky, Avihu Dekel, Hagai Aronowitz +2

Spoken meaning often depends not only on what is said, but also on which word is emphasized. The same sentence can convey correction, contrast, or clarification depending on where…

cs.CL2026

LaMI: Augmenting Large Language Models via Late Multi-Image Fusion

Guy Yariv, Idan Schwartz, Yossi Adi +1

Commonsense reasoning often requires both textual and visual knowledge, yet Large Language Models (LLMs) trained solely on text lack visual grounding (e.g., "what color is an emper…

cs.CL20263 cited

On The Landscape of Spoken Language Models: A Comprehensive Survey

Siddhant Arora, Kai-Wei Chang, Chung-Ming Chien +7

The field of spoken language processing is undergoing a shift from training custom-built, task-specific models toward using and optimizing spoken language models (SLMs) which act a…

cs.CL2025

DM-Codec: Distilling Multimodal Representations for Speech Tokenization

Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin +6

Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional…

cs.CL2025

Scaling Analysis of Interleaved Speech-Text Language Models

Gallil Maimon, Michael Hassid, Amit Roth +1

Existing Speech Language Model (SLM) scaling analysis paints a bleak picture. It predicts that SLMs require much more compute and data compared to text, leading some to question th…

cs.CL2025

ZeroSumEval: An Extensible Framework For Scaling LLM Evaluation with Inter-Model Competition

Hisham A. Alyahya, Haidar Khan, Yazeed Alnumay +2

We introduce ZeroSumEval, a dynamic, competition-based, and evolving evaluation framework for Large Language Models (LLMs) that leverages competitive games. ZeroSumEval encompasses…