Natural Language Processing

FM-Delta: Lossless compression halves cloud storage for massive fine-tuned language models, saving costs without sacrificing accuracy.

FlowLLM revolutionizes material design by cleverly merging large language models and Riemannian flow matching, yielding a 300% boost in stable material generation!

FLORA enables efficient & private federated fine-tuning of LLMs via novel stacking-based heterogeneous low-rank adaptation, surpassing existing methods.

FLAME: A novel alignment method enhances large language model factuality by addressing hallucination in supervised fine-tuning and reinforcement learning, resulting in more accurate and helpful AI ass…

Researchers crack the code of in-context learning in Transformers, revealing how architecture, low-rank parameters, and data correlations influence model optimization and generalization.

Prompt Adversarial Tuning (PAT) defends against LLM jailbreaking by training a protective prompt prefix. PAT uses adversarial and benign prompts to optimize this prefix, significantly reducing succes…

FlexLoRA: Efficient Federated Fine-tuning of LLMs for Heterogeneous Tasks and Resources.

FASTopic: a pretrained transformer-based topic model achieving superior speed, adaptivity, stability, and transferability compared to existing methods.

Accelerated discrete diffusion model sampling is achieved via novel discrete non-Markov diffusion models (DNDM) with predetermined transition times, enabling a training-free algorithm that significant…

Speculative Rejection: A novel algorithm boosts Large Language Model (LLM) alignment by speeding up inference-time alignment by 16-32x!

LLM-Infused Diffuser boosts text-to-image generation by smartly integrating LLMs, surpassing existing models in prompt understanding and image quality.

LLM quantization, while improving efficiency, creates a security risk: attackers can craft seemingly benign models that exhibit malicious behavior only when quantized.

D2DMoE boosts Transformer efficiency by up to 60% via smart activation sparsity and dynamic expert selection, outperforming existing methods.

This paper introduces a model-agnostic algorithm that uses natural language predicates to make statistical model parameters directly interpretable, significantly improving explainability.

DEVIL: a novel text-to-video evaluation protocol focusing on video dynamics, resulting in more realistic video generation.

New method estimates hallucination rates in generative AI’s in-context learning, improving model reliability.

ESPACE: A novel LLM compression technique achieving 50% model size reduction with minimal accuracy loss by cleverly projecting activations onto principal components.

LLM4EA: A novel framework efficiently merges knowledge graphs using LLMs, overcoming noisy annotations and high costs via active learning and unsupervised label refinement, boosting accuracy and effic…

Boosting AI reasoning! New research enhances LLMs’ logical abilities via a principled synthetic logic corpus, achieving substantial improvements across logic, math, and coding benchmarks.

Boosting LLM trustworthiness, researchers introduce Sparse Activation Control, a training-free method that concurrently enhances safety, factuality, and bias mitigation by selectively controlling atte…