# LG AI Research — [ACL 2024] Towards Efficient Large Language Models

- Company: LG AI Research (lgresearch.ai)
- Announced: 2024-09-10T00:00:00+00:00
- Category: research-paper
- Subject: EXAONE
- Source: https://www.lgresearch.ai/blog/view?seq=478
- Record: https://forck.live/items/4830-acl-2024-towards-efficient-large-language-models

LG AI Research blog post summarizing the LayerSkip method from Meta presented at ACL 2024, which uses layer dropout, early exit loss, and self-speculative decoding to speed up LLM inference.

## Evidence

Verbatim from https://www.lgresearch.ai/blog/view?seq=478:

> In this blog, we will explore various approaches to enhancing the efficiency of large language models (LLMs), with a focus on research presented at ACL 2024.

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Record: https://forck.live/items/4830-acl-2024-towards-efficient-large-language-models
Catalogue: https://forck.live/llms.txt
Feed: https://forck.live/feed.md