arXiv AI
3d ago
Where did the ambiguity go? Examining how multimodal models interpret polysemous words
arXiv:2608. 00410v2 Announce Type: replace Abstract: Human language is highly polysemous.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
3d ago
Decode-Branch Transformers: Decoupling the Primary Prefill Path from Additional Decode Computation
arXiv:2608. 12385v2 Announce Type: replace Abstract: As large language models serve ever more requests, cumulative inference cost is growing relative to the one-time cost of training.
arXiv AI
3d ago
MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4
arXiv:2406. 00971v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have recently seen significant advancements through integrating with Large Language Models (LLMs).
arXiv AI
3d ago
Quantum Large Language Models via Tensor Network Disentanglers
arXiv:2410. 17397v2 Announce Type: replace-cross Abstract: We introduce a framework for seamlessly integrating quantum computing into pretrained large language models (LLMs).
arXiv AI
3d ago
Rethinking Token-wise Feature Caching: Accelerating Diffusion Transformers with Dual Feature Caching
arXiv:2412. 18911v3 Announce Type: replace-cross Abstract: Diffusion Transformers (DiT) have become the dominant methods in image and video generation yet still suffer substantial computational costs.
arXiv AI
3d ago
Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities
arXiv:2501. 12147v2 Announce Type: replace-cross Abstract: Selecting appropriate training data is crucial for instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve balanced performance across different tasks.
arXiv AI
3d ago
DR.GAP: Mitigating Bias in Large Language Models using Gender-Aware Prompting with Decoupled Reasoning
arXiv:2502. 11603v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit strong natural language understanding capabilities but also inherit and amplify societal biases, particularly gender bias, raising fairness concerns.
arXiv AI
3d ago
Thinking Outside the (Gray) Box: A Context-Based Score for Assessing Value and Originality in Neural Text Generation
arXiv:2502. 13207v4 Announce Type: replace-cross Abstract: Despite the increasing use of large language models for creative tasks, their outputs often lack diversity.
arXiv AI
3d ago
Bye-bye, Bluebook? Automating Legal Drudgery With AI-Augmented Rule Following
arXiv:2505. 02763v2 Announce Type: replace-cross Abstract: One of the central promises of legal AI is to automate drudgery -- the formal, repetitive tasks of lawyers' work that consume time without calling for much discretion.
arXiv AI
3d ago
From Prompts to Constructs: A Dual-Validity Framework for Large Language Model Research in Psychology
arXiv:2506. 16697v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are entering psychological research both as tools and as objects of inquiry.
arXiv AI
3d ago
CulTrace: Tracing Internal Cultural Reasoning in Large Language Models
arXiv:2508. 08879v3 Announce Type: replace-cross Abstract: The growing deployment of large language models (LLMs) across diverse cultural contexts necessitates a deeper understanding of models' hidden representations of different cultures.
arXiv Machine Learning
3d ago
Le Critique: Privileged Value Functions for LLM Reinforcement Learning
arXiv:2608. 16739v1 Announce Type: new Abstract: Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy.
arXiv Machine Learning
3d ago
On the Principles Behind Neural Network Optimizers
arXiv:2608. 16760v1 Announce Type: new Abstract: Reliable optimization is central to neural network (NN) training, yet Adam, the default optimizer for modern LLMs, rests on a fragile foundation.
arXiv Machine Learning
3d ago
Belayer: Efficient Fault Tolerance for LLM Agentic RL Training
arXiv:2608. 14635v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly trained with reinforcement learning in long-horizon, sandboxed environments.
arXiv Machine Learning
3d ago
Training Leaves Traces: Centered Residual Signatures for Language Model Lineage Verification
arXiv:2608. 14929v1 Announce Type: cross Abstract: Open-weight language models are fine-tuned, quantized, pruned, and merged, yet their provenance is often undocumented.
arXiv Machine Learning
3d ago
NPU Offloading of a Frozen Visual Encoder for Robot Policy Training
arXiv:2608. 15002v1 Announce Type: cross Abstract: When a robot policy is trained for a new task or dataset, its visual encoder can be frozen and only its action generation module trained, reducing training cost.
arXiv Machine Learning
3d ago
Global Simulation-Guided Dynamic Operator Scheduling for Efficient Multi-Tenant Model Serving
arXiv:2608. 15762v1 Announce Type: cross Abstract: Container-granularity scheduling leaves abundant short-lived idle slices within containers unexploited.
arXiv Machine Learning
3d ago
PWLR: Pairwise Witness Local Rejection for Boundary-Aware Out-of-Distribution Detection
arXiv:2608. 15802v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries.
arXiv Machine Learning
3d ago
CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration
arXiv:2608. 16268v1 Announce Type: cross Abstract: Medical foundation models improve generalization when training AI models with limited labeled data, but remain confined to a single specialty, such as pathology or radiology, and to either sparse or dense outputs, such as classification or segmentation.
arXiv AI
3d ago
A Large-Scale Chinese Knowledge Graph-Text Alignment Dataset for Benchmarking Knowledge-Grounded LLMs
arXiv:2510. 06039v2 Announce Type: replace-cross Abstract: Reliable evaluation of knowledge-grounded Large Language Models (LLMs) in Chinese requires resources that explicitly align Chinese-language text with verifiable Knowledge Graph (KG) facts.