arXiv Machine Learning
Aug 28

Squeezing More from Limited Data with Recursive Transformers

The paper investigates how to effectively pre‑train language models when the data budget is limited but compute is plentiful. It shows that increasing model size only improves performance up to an optimal point, after which overfitting degrades generalization, and that this optimal size varies with both the data budget and downstream tasks. To overcome the inefficiencies of standard Transformers in this regime, the authors propose recursive Transformers that reuse a shared block across depth and employ factorized embeddings, achieving better results than standard models on 10M–100M word pre‑training budgets and competitive performance with BabyLM Challenge 2025 winners.

By Serdar G\"ulbahar, Lukas Edman, Alexander Fraser
arXiv AI
Sep 10

FastE: Readout-Triggered Token Compression for LLM Embedding Inference

FastE is a training‑free, plug‑and‑play method that compresses token prefixes in large language model (LLM) embedding inference. It uses a shared fixed threshold on batch‑mean readout‑prefix alignment to decide when to compress and ranks prefix states by readout attention scores to keep the most important ones. Experiments on Qwen3‑Embedding models show that FastE can cut decoder‑backbone FLOPs by over 40% while preserving more than 99% of the original ranking quality across multiple benchmarks and tasks.

By Jinsong Shu, Jinyong Wen, Baokun Wang, Zhongle Xie, Lidan Shou, Weiqiang Wang, Gang Chen
arXiv Computation and Language
3d ago

When Is 0.1% Enough? Analyzing the Combined Effects of Dimensionality Reduction and Quantization on Text Embedding Compression

The paper investigates how dimensionality reduction and quantization together affect text embedding compression across four task families in the Massive Text Embedding Benchmark using five pretrained models. It finds that combining these techniques can preserve near-original performance while drastically reducing storage, with some classification models retaining at least 99% of their score using less than 0.1% of the original storage. The optimal trade‑off between dimensionality and bit‑width varies by task, with retrieval tasks being more sensitive to compression.

By Riku Kisako, Hayato Tsukagoshi, Ryohei Sasano