6 papers
CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models
Guangzhi Sun, Xiao Zhan, Shutong Feng +2
Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption. Current LLM safety benchmarks often focus solely on the refu…
Measuring the Redundancy of Decoder Layers in SpeechLLMs
Adel Moumen, Guangzhi Sun, Philip C Woodland
Speech Large Language Models route speech encoder representations into an LLM decoder that typically accounts for over 90% of total parameters. We study how much of this decoder ca…
Protecting Bystander Privacy via Selective Hearing in Audio LLMs
Xiao Zhan, Guangzhi Sun, Jose Such +1
Audio Large language models (LLMs) are increasingly deployed in the real world, where they inevitably capture speech from unintended nearby bystanders, raising privacy risks that e…
Audio-Conditioned Diffusion LLMs for ASR and Deliberation Processing
Mengqi Wang, Zhan Liu, Zengrui Jin +3
Diffusion-based large language models (DLLMs) have recently attracted growing interest as an alternative to autoregressive decoders. In this work, we present an empirical study on…
Cross-Lingual Interleaving for Speech Language Models
Adel Moumen, Guangzhi Sun, Philip C. Woodland
Spoken Language Models (SLMs) aim to learn linguistic competence directly from speech using discrete units, widening access to Natural Language Processing (NLP) technologies for la…
SkillAggregation: Reference-free LLM-Dependent Aggregation
Guangzhi Sun, Anmol Kagrecha, Potsawee Manakul +2
Large Language Models (LLMs) are increasingly used to assess NLP tasks due to their ability to generate human-like judgments. Single LLMs were used initially, however, recent work…