Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Ok, Jungseul"'
Large language models (LLMs) have shown remarkable versatility across tasks, but aligning them with individual human preferences remains challenging due to the complexity and diversity of these preferences. Existing methods often overlook the fact th
Externí odkaz:
http://arxiv.org/abs/2411.00524
Retrieval-augmented generation (RAG) addresses key limitations of large language models (LLMs), such as hallucinations and outdated knowledge, by incorporating external databases. These databases typically consult multiple sources to encompass up-to-
Externí odkaz:
http://arxiv.org/abs/2410.22954
Federated fine-tuning for Large Language Models (LLMs) has recently gained attention due to the heavy communication overhead of transmitting large model updates. Low Rank Adaptation (LoRA) has been proposed as a solution, yet its application in feder
Externí odkaz:
http://arxiv.org/abs/2410.22815
Deep learning-based expert models have reached superhuman performance in decision-making domains such as chess and Go. However, it is under-explored to explain or comment on given decisions although it is important for human education and model expla
Externí odkaz:
http://arxiv.org/abs/2410.20811
We study the problem of training an unbiased and accurate model given a dataset with multiple biases. This problem is challenging since the multiple biases cause multiple undesirable shortcuts during training, and even worse, mitigating one may exace
Externí odkaz:
http://arxiv.org/abs/2409.03303
We address a practical scenario of anomaly detection for industrial sound data, where the sound of a target machine is corrupted by background noise and interference from neighboring machines. Overcoming this challenge is difficult since the interfer
Externí odkaz:
http://arxiv.org/abs/2409.01885
Research on hate speech has predominantly revolved around detection and interpretation from textual inputs, leaving verbal content largely unexplored. While there has been limited exploration into hate speech detection within verbal acoustic speech i
Externí odkaz:
http://arxiv.org/abs/2408.06065
Remarkable advances in large language models (LLMs) have enabled high-quality text summarization. However, this capability is currently accessible only through LLMs of substantial size or proprietary LLMs with usage fees. In response, smaller-scale L
Externí odkaz:
http://arxiv.org/abs/2406.04625
The evaluation of summary quality encompasses diverse dimensions such as consistency, coherence, relevance, and fluency. However, existing summarization methods often target a specific dimension, facing challenges in generating well-balanced summarie
Externí odkaz:
http://arxiv.org/abs/2406.00303
Recent image tone adjustment (or enhancement) approaches have predominantly adopted supervised learning for learning human-centric perceptual assessment. However, these approaches are constrained by intrinsic challenges of supervised learning. Primar
Externí odkaz:
http://arxiv.org/abs/2404.01123