5 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…
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…
Building Better AI Agents: A Provocation on the Utilisation of Persona in LLM-based Conversational Agents
Guangzhi Sun, Xiao Zhan, Jose Such
The incorporation of Large Language Models (LLMs) such as the GPT series into diverse sectors including healthcare, education, and finance marks a significant evolution in the fiel…