Large Language Model
Reranking Laws for Language Generation: A Communication-Theoretic Perspective
To ensure large language models (LLMs) are used safely, one must reduce their propensity to hallucinate or to generate unacceptable answers. A simple and often used strategy is to first let the LLM generate multiple hypotheses and then employ a reranker to choose the best one.
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning
Large vision-language models (VLMs) fine-tuned on specialized visual instruction-following data have exhibited impressive language reasoning capabilities across various scenarios.
Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning
Accurate emotion perception is crucial for various applications, including human-computer interaction, education, and counseling.
GAVEL: Generating Games Via Evolution and Language Models Graham T odd New York University Tandon Brooklyn, New York, USA gdrtodd Alexander G. Padula ETH Zurich Zurich, Switzerland apadula
Automatically generating novel and interesting games is a complex task.
Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization
Out-of-Distribution (OOD) generalization in machine learning is a burgeoning area of study.
StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation Y upeng Zhou 1 Daquan Zhou 2 Ming-Ming Cheng
It is trained to estimate the motion conditions between two provided images in the semantic spaces.
Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
LLMs, correcting errors or outdated responses while ensuring no negative impacts are created.
Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency
This work proposes a Bias-V olatility Framework (BVF) that estimates the probability distribution function of LLM stereotypes.
VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation
A well-known dilemma in large vision-language models ( e.g., GPT -4, LLaV A) is that while increasing the number of vision tokens generally enhances visual