Large Language
Large language models (LLMs) are rapidly advancing artificial intelligence, aiming to create systems capable of understanding and generating human-like text. Current research focuses on improving efficiency (e.g., through speculative decoding), exploring their intriguing properties in multimodal contexts (combining language with vision), and applying them to diverse fields like healthcare, manufacturing, and software engineering. This work is significant because LLMs are already impacting various sectors, offering potential for improved decision-making, automation, and personalized experiences, while also raising important questions about robustness, security, and ethical implications.
111papers
Papers - Page 3
April 20, 2025
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions
SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs
AI with Emotions: Exploring Emotional Expressions in Large Language Models
April 18, 2025
April 15, 2025
Masculine Defaults via Gendered Discourse in Podcasts and Large Language Models
Reimagining Urban Science: Scaling Causal Inference with Large Language Models
Propaganda via AI? A Study on Semantic Backdoors in Large Language Models
Dynamic Compressing Prompts for Efficient Inference of Large Language Models
Large Language Model-Informed Feature Discovery Improves Prediction and Interpretation of Credibility Perceptions of Visual Content
LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation
April 14, 2025
April 11, 2025