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pro vyhledávání: '"DONG, YUE"'
Key-Value (KV) caching is a common technique to enhance the computational efficiency of Large Language Models (LLMs), but its memory overhead grows rapidly with input length. Prior work has shown that not all tokens are equally important for text gen
Externí odkaz:
http://arxiv.org/abs/2410.19258
Language models (LMs) possess a strong capability to comprehend natural language, making them effective in translating human instructions into detailed plans for simple robot tasks. Nevertheless, it remains a significant challenge to handle long-hori
Externí odkaz:
http://arxiv.org/abs/2409.20560
Recent studies show that text-to-image (T2I) models are vulnerable to adversarial attacks, especially with noun perturbations in text prompts. In this study, we investigate the impact of adversarial attacks on different POS tags within text prompts o
Externí odkaz:
http://arxiv.org/abs/2409.15381
Personalized conversational information retrieval (CIR) combines conversational and personalizable elements to satisfy various users' complex information needs through multi-turn interaction based on their backgrounds. The key promise is that the per
Externí odkaz:
http://arxiv.org/abs/2407.16192
Large language models (LLMs) have fundamentally transformed artificial intelligence, catalyzing recent advancements while imposing substantial environmental and computational burdens. We introduce TRAWL (Tensor Reduced and Approximated Weights for La
Externí odkaz:
http://arxiv.org/abs/2406.17261
Query expansion has been employed for a long time to improve the accuracy of query retrievers. Earlier works relied on pseudo-relevance feedback (PRF) techniques, which augment a query with terms extracted from documents retrieved in a first stage. H
Externí odkaz:
http://arxiv.org/abs/2406.07136
Autor:
Cai, Zefan, Zhang, Yichi, Gao, Bofei, Liu, Yuliang, Liu, Tianyu, Lu, Keming, Xiong, Wayne, Dong, Yue, Chang, Baobao, Hu, Junjie, Xiao, Wen
In this study, we investigate whether attention-based information flow inside large language models (LLMs) is aggregated through noticeable patterns for long context processing. Our observations reveal that LLMs aggregate information through Pyramida
Externí odkaz:
http://arxiv.org/abs/2406.02069
Autor:
Chakraborty, Trishna, Shayegani, Erfan, Cai, Zikui, Abu-Ghazaleh, Nael, Asif, M. Salman, Dong, Yue, Roy-Chowdhury, Amit K., Song, Chengyu
Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing safety training techniques like Supervised Fine-tuning (SFT) and Reinf
Externí odkaz:
http://arxiv.org/abs/2406.02575
Recent studies reveal that Large Language Models (LLMs) face challenges in balancing safety with utility, particularly when processing long texts for NLP tasks like summarization and translation. Despite defenses against malicious short questions, th
Externí odkaz:
http://arxiv.org/abs/2405.15202
Multi-band gravitational-wave (GW) standard siren observations are poised to herald a new era in the study of cosmic evolution. These observations offer higher signal-to-noise ratios and improved localizations compared to those achieved with single-b
Externí odkaz:
http://arxiv.org/abs/2404.18188