9 papers · 1 filter
Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models
Hyunjong Ok, Jaeho Lee
Large language models exhibit surprising sensitivity to the structure of the prompt, but the mechanisms underlying this sensitivity remain poorly understood. In this work, we condu…
Speculative End-Turn Detector for Efficient Speech Chatbot Assistant
Hyunjong Ok, Suho Yoo, Jaeho Lee
Spoken dialogue systems powered by large language models have demonstrated remarkable abilities in understanding human speech and generating appropriate spoken responses. However,…
AuditoryBench++: Can Language Models Understand Auditory Knowledge without Hearing?
Hyunjong Ok, Suho Yoo, Hyeonjun Kim +1
Even without directly hearing sounds, humans can effortlessly reason about auditory properties, such as pitch, loudness, or sound-source associations, drawing on auditory commonsen…
S2Cap: A Benchmark and a Baseline for Singing Style Captioning
Hyunjong Ok, Jaeho Lee
Singing voices contain much richer information than common voices, including varied vocal and acoustic properties. However, current open-source audio-text datasets for singing voic…
Prompt-based Depth Pruning of Large Language Models
Juyun Wee, Minjae Park, Jaeho Lee
Depth pruning aims to reduce the inference cost of a large language model without any hardware-specific complications, by simply removing several less important transformer blocks.…
Imagine to Hear: Auditory Knowledge Generation can be an Effective Assistant for Language Models
Suho Yoo, Hyunjong Ok, Jaeho Lee
Language models pretrained on text-only corpora often struggle with tasks that require auditory commonsense knowledge. Previous work addresses this problem by augmenting the langua…