arXiv AI
NGM: A Plug-and-Play Training-Free Memory Module for LLMs
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arXiv Machine Learning
Oct 15
The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation
The paper introduces COUNT, a method that builds n‑gram embeddings without adding learned parameters by storing precomputed n‑gram counts on host memory. By weighting the language model’s own unembeddings with corpus frequencies, COUNT can be added as an extra input embedding during pretraining, improving validation loss equivalent to a 57% increase in compute for a 1B‑parameter model. Additionally, COUNT enables efficient GPU memory usage and allows domain‑specific n‑gram tables to be swapped in at inference time for training‑free adaptation.
arXiv Computation and Language
Oct 23
ULME: Predictive Coupling for Mutually Reinforcing Embedding and Language Modeling
ULME is a post‑training framework that unifies text embedding and language modeling within a single causal decoder‑only LLM by inserting learnable slots for semantic and reasoning embeddings. It trains three objectives—contrastive loss for semantic alignment, language‑modeling loss for answer generation, and a stop‑gradient predictive loss linking reasoning to the answer—so that generation actively shapes the embedding. Across Qwen2.5 and Qwen3 models (0.5B–8B), ULME improves MTEB scores by 3.3–4.1 points, tightens BRIGHT and AdvBench‑IR, and matches generation‑only fine‑tunes on standard accuracy benchmarks.
Hugging Face Trending Papers
Oct 22
ULME: Predictive Coupling for Mutually Reinforcing Embedding and Language Modeling
ULME introduces a post‑training framework that unifies text embedding and generation within a single causal decoder‑only LLM by inserting learnable slots for semantic and reasoning embeddings. It trains three objectives—contrastive, language‑modeling, and stop‑gradient predictive—so that generation actively shapes the embedding space. Across Qwen2.5 and Qwen3 models (0.5B–8B), ULME improves MTEB scores by 3.3–4.1 points, tightens BRIGHT and AdvBench‑IR, and matches generation‑only fine‑tunes on standard accuracy benchmarks.
arXiv AI
Oct 1
Frozen Memory Is Not Enough: Rethinking External Memory as Extraction
arXiv:2608.17050v3 Announce Type: replace-cross Abstract: Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access...
arXiv AI
Oct 15
History Replays Itself: Unleashing Long-Context Capability in Linear Attention
arXiv:2610.15715v1 Announce Type: new Abstract: Linear attention promises the efficiency needed for long-context language modelling, yet its fixed-size recurrent state must simultaneously support ong...
arXiv AI
Jul 31
ARC-Encoder: learning compressed text representations for large language models
arXiv:2510. 20535v2 Announce Type: replace-cross Abstract: Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs.