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
The paper presents a comprehensive evaluation of post‑training quantization (PTQ) techniques for large language models (LLMs), addressing gaps in prior work by conducting fair, large‑scale experiments on text‑only LLMs. It decouples quantization into pre‑quantization transformation and error mitigation, systematically studies granularity, symmetry, and data formats (MXFP4, NVFP4), and finds that rotation‑based transformations and low‑rank compensation with GPTQ can improve performance, while finer granularity and asymmetric quantization benefit activations more than weights. The study highlights the importance of understanding component interactions and the impact of scaling factor formats on low‑bit quantization performance.
By Yutong Liu, Cairong Zhao, Guosheng Hu
arXiv:2505.10202v2 Announce Type: replace
Abstract: Large Language Models (LLMs) have achieved remarkable success but face significant computational and memory challenges, particularly due to their e...
By Jintian Shao, Hongyi Huang, Jiayi Wu, YiMing Cheng, ZhiYu Wu, You Shan, MingKai Zheng
LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone models slow down embedding inference, while high-dimensional full-precision embeddings impose substantial storage and bandwidth overhead on large-scale indexes. In this paper, we present BITEMBED, an extreme low-bit framework for LLM-based text embedding that jointly targets encoding efficiency and vector storage.
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
The paper investigates reducing memory usage of n‑gram embedding tables in open‑weight language models by storing them in low precision. Experiments on nanochat, Llama, and GPT‑2 families show that four‑bit tables cut table memory by about 75% while retaining over 99% of the validation‑loss improvement. The study also demonstrates that re‑allocating the saved memory to larger tables can surpass the performance of full‑precision tables, and that similar savings are achievable in large models like DeepSeek‑V4.1‑Flash and Qwen3.8‑Flash‑Next with minimal impact on perplexity and downstream tasks.
By Stephen Chung, Wenyu Du