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Challenges and applications of large language models
Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …
[PDF][PDF] Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects
Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …
Efficient large language models: A survey
Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …
tasks such as natural language understanding and language generation, and thus have the …
Towards efficient generative large language model serving: A survey from algorithms to systems
In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …
A survey on efficient inference for large language models
Large Language Models (LLMs) have attracted extensive attention due to their remarkable
performance across various tasks. However, the substantial computational and memory …
performance across various tasks. However, the substantial computational and memory …
Loongserve: Efficiently serving long-context large language models with elastic sequence parallelism
The context window of large language models (LLMs) is rapidly increasing, leading to a
huge variance in resource usage between different requests as well as between different …
huge variance in resource usage between different requests as well as between different …
Minference 1.0: Accelerating pre-filling for long-context llms via dynamic sparse attention
The computational challenges of Large Language Model (LLM) inference remain a
significant barrier to their widespread deployment, especially as prompt lengths continue to …
significant barrier to their widespread deployment, especially as prompt lengths continue to …
Advancing transformer architecture in long-context large language models: A comprehensive survey
Transformer-based Large Language Models (LLMs) have been applied in diverse areas
such as knowledge bases, human interfaces, and dynamic agents, and marking a stride …
such as knowledge bases, human interfaces, and dynamic agents, and marking a stride …
Zipvl: Efficient large vision-language models with dynamic token sparsification and kv cache compression
The efficiency of large vision-language models (LVLMs) is constrained by the computational
bottleneck of the attention mechanism during the prefill phase and the memory bottleneck of …
bottleneck of the attention mechanism during the prefill phase and the memory bottleneck of …
Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …