When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks

4 February 2025

Papers citing "When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks"

8 / 8 papers shown

Title
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models Chankyu Lee Rajarshi Roy Mengyao Xu Jonathan Raiman Mohammad Shoeybi Bryan Catanzaro Ming-Yu Liu RALM 132 170 0 27 May 2024
Graph-enhanced Large Language Models in Asynchronous Plan Reasoning Fangru Lin Emanuele La Malfa Valentin Hofmann Elle Michelle Yang Anthony Cohn J. Pierrehumbert LRM 92 17 0 05 Feb 2024
AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts Tongshuang Wu Michael Terry Carrie J. Cai LLMAG AI4CE LRM 56 450 0 04 Oct 2021
MPNet: Masked and Permuted Pre-training for Language Understanding Kaitao Song Xu Tan Tao Qin Jianfeng Lu Tie-Yan Liu 74 1,093 0 20 Apr 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 378 4,662 0 23 Jan 2020
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Nils Reimers Iryna Gurevych 489 11,979 0 27 Aug 2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova VLM SSL SSeg 784 93,936 0 11 Oct 2018
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Leland McInnes John Healy James Melville 86 9,312 0 09 Feb 2018