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cs.CL2025
Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding
Zhuoming Chen, Avner May, Ruslan Svirschevski +4
As the usage of large language models (LLMs) grows, performing efficient inference with these models becomes increasingly important. While speculative decoding has recently emerged…
cs.CL2024
Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements
Anton Voronov, Lena Wolf, Max Ryabinin
Large language models demonstrate a remarkable capability for learning to solve new tasks from a few examples. The prompt template, or the way the input examples are formatted to o…
cs.CL2024
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
Giwon Hong, Aryo Pradipta Gema, Rohit Saxena +8
Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text. However, the…