3 papers
cs.LG2025
FLoRA: Fused forward-backward adapters for parameter efficient fine-tuning and reducing inference-time latencies of LLMs
Dhananjaya Gowda, Seoha Song, Junhyun Lee +1
As the large language models (LLMs) grow in size each day, efficient training and fine-tuning has never been as important as nowadays. This resulted in the great interest in parame…
cs.CL2025
zFLoRA: Zero-Latency Fused Low-Rank Adapters
Dhananjaya Gowda, Seoha Song, Harshith Goka +1
Large language models (LLMs) are increasingly deployed with task-specific adapters catering to multiple downstream applications. In such a scenario, the additional compute associat…
cs.CL2025
BRIDO: Bringing Democratic Order to Abstractive Summarization
Junhyun Lee, Harshith Goka, Hyeonmok Ko
Hallucination refers to the inaccurate, irrelevant, and inconsistent text generated from large language models (LLMs). While the LLMs have shown great promise in a variety of tasks…