4 papers
Bias and Fairness in Large Language Models: A Survey
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touc…
RHS-TRNG: A Resilient High-Speed True Random Number Generator Based on STT-MTJ Device
Siqing Fu, Tiejun Li, Chunyuan Zhang +5
High-quality random numbers are very critical to many fields such as cryptography, finance, and scientific simulation, which calls for the design of reliable true random number gen…
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
Yanzhe Zhang, Ruiyi Zhang, Jiuxiang Gu +4
Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans. Furthermore, recent instruction-following datasets include images as visu…
Towards Building the Federated GPT: Federated Instruction Tuning
Jianyi Zhang, Saeed Vahidian, Martin Kuo +6
While "instruction-tuned" generative large language models (LLMs) have demonstrated an impressive ability to generalize to new tasks, the training phases heavily rely on large amou…