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Found 5 papers, 2 papers with code
Distributed Speculative Inference of Large Language Models
In practice, off-the-shelf LLMs often do not have matching drafters that are sufficiently fast and accurate.
Accelerating Speculative Decoding using Dynamic Speculation Length
Speculative decoding is a promising method for reducing the inference latency of large language models.
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity
This approach focuses the reasoning process on generating an attribution-centric output.
Optimizing Retrieval-augmented Reader Models via Token Elimination
Fusion-in-Decoder (FiD) is an effective retrieval-augmented language model applied across a variety of open-domain tasks, such as question answering, fact checking, etc.
How to Train BERT with an Academic Budget
While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford.
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