In-context Autoencoder for Context Compression in Large Language Models

Concetti Chiave

Proposing the In-context Autoencoder (ICAE) to compress long contexts efficiently in large language models.

Sintesi

The article introduces the In-context Autoencoder (ICAE) as a solution to compress long contexts efficiently in large language models. ICAE leverages autoencoding and language modeling objectives to generate memory slots that represent the original context accurately. Pretraining on massive text data enables ICAE to achieve 4× context compression based on Llama, improving latency and GPU memory cost during inference. The results suggest a novel perspective on working memory in cognitive science and representation learning in LLMs, emphasizing scalability and potential implications for further research.
As artificial intelligence advances, concerns about ethical use and public perception arise among tech companies like Google, Amazon, Microsoft, Facebook, and IBM. Long context modeling poses challenges for Transformer-based LLMs due to their self-attention mechanism. Previous research focuses on architectural innovations to tackle long context issues but struggles with performance on lengthy contexts. In contrast, ICAE approaches the problem through context compression using a learnable encoder adapted from LLM with LoRA for encoding into memory slots.
Experiments demonstrate ICAE's effectiveness in compressing contexts while maintaining accuracy. The model is fine-tuned on instruction data for practical scenarios, enhancing interaction with prompts. Results show improved autoencoding performance of pretrained ICAE without instruction fine-tuning. Text continuation evaluation indicates more pronounced losses with higher compression ratios in language modeling tasks.

Statistiche

Introducing about 1% additional parameters
Achieving 4× context compression based on Llama
Improved latency and GPU memory cost during inference

Citazioni

"Context compression is motivated by the fact that a text can be represented in different lengths in an LLM while conveying the same information."
"We propose In-context Autoencoder (ICAE) – a novel approach to context compression by leveraging the power of an LLM."
"All these results imply a novel perspective on the connection between working memory in cognitive science and representation learning in LLMs."

Approfondimenti chiave tratti da

In-context Autoencoder for Context Compression in a Large Language Model

by Tao Ge,Jing ... alle arxiv.org 03-19-2024

https://arxiv.org/pdf/2307.06945.pdf

Domande più approfondite

How does ICAE's approach to context compression compare to other methods like prompt compression

ICAE's approach to context compression differs from methods like prompt compression in its focus on compressing long contexts into memory slots that can be directly conditioned on by the target LLM. Prompt compression, on the other hand, aims to learn compact soft prompts to simulate the original natural language prompt. While both approaches involve compressing information for efficient processing by language models, ICAE's method specifically targets handling long contexts by generating memory slots that represent the original context in a more concise manner. This allows for improved efficiency and reduced computational overhead during inference compared to traditional prompt compression techniques.

What are the potential implications of ICAE's scalability for handling longer contexts

The scalability of ICAE has significant implications for handling longer contexts efficiently. As demonstrated in the study, ICAE's performance is expected to improve with more powerful target LLMs, enabling even greater context compression ratios. This scalability opens up possibilities for effectively managing and processing extremely lengthy texts or inputs without compromising model performance or accuracy. With advancements in AI research focusing on larger and stronger LLMs, ICAE's ability to handle longer contexts could prove invaluable in various applications requiring complex text understanding and generation tasks.

How might understanding memorization patterns of LLMs contribute to advancements in AI research beyond context management

Understanding memorization patterns of LLMs can lead to advancements beyond context management in AI research. By gaining insights into how these models encode and retain information within compressed memory slots, researchers can potentially enhance model training strategies, optimize memory utilization, and improve overall learning efficiency. Additionally, this understanding may shed light on cognitive processes related to human memory encoding and retrieval mechanisms, offering valuable parallels between artificial intelligence systems and human cognition. Leveraging these insights could pave the way for developing more effective learning algorithms inspired by principles observed in both machine learning models and human brain functions.