ARC-Encoder: learning compressed text representations for large language
  models
Authors: name: Hippolyte Pilchen, name: Edouard Grave, name: Patrick Pérez
Organization: Kyutai

Recent techniques such as retrieval-augmented generation or chain-of-thought
reasoning have led to longer contexts and increased inference costs. Context
compression techniques can reduce these costs, but the most effective
approaches require fine-tuning the target model or even modifying its
architecture. This can degrade its general abilities when not used for this
specific purpose. Here we explore an alternative approach: an encoder that
compresses the context into continuous representations which replace token
embeddings in decoder LLMs. First, we perform a systematic study of training
strategies and architecture choices for the encoder. Our findings led to the
design of an Adaptable text Representations Compressor, named ARC-Encoder,
which outputs x-times fewer continuous representations (typically
x!in!{4,8}) than text tokens. We evaluate ARC-Encoder across a variety
of LLM usage scenarios, ranging from in-context learning to context window
extension, on both instruct and base decoders. Results show that ARC-Encoder
achieves state-of-the-art performance on several benchmarks while improving
computational efficiency at inference. Finally, we demonstrate that our models
can be adapted to multiple decoders simultaneously, allowing a single encoder
to generalize across different decoder LLMs. This makes ARC-Encoder a flexible
and efficient solution for portable encoders that work seamlessly with multiple
LLMs. We release a training code at https://github.com/kyutai-labs/ARC-Encoder
, fine-tuning dataset and pretrained models are available at
https://huggingface.co/collections/kyutai/arc-encoders-68ee18787301407d60a57047 .