1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
SpecHub: Provable Acceleration to Multi-Draft Speculative Decoding
Ryan Sun, Tianyi Zhou, Xun Chen +1
Large Language Models (LLMs) have become essential in advancing natural language processing (NLP) tasks, but their sequential token generation limits inference speed. Multi-Draft S…
BenTo: Benchmark Task Reduction with In-Context Transferability
Hongyu Zhao, Ming Li, Lichao Sun +1
Evaluating large language models (LLMs) is costly: it requires the generation and examination of LLM outputs on a large-scale benchmark of various tasks. This paper investigates ho…
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?
Yue Huang, Chenrui Fan, Yuan Li +4
Large Language Models (LLMs) have garnered significant attention due to their remarkable ability to process information across various languages. Despite their capabilities, they e…
DataGen: Unified Synthetic Dataset Generation via Large Language Models
Yue Huang, Siyuan Wu, Chujie Gao +8
Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expen…