LLM Survey Papers
A curated collection of influential survey papers on Large Language Models, covering foundational concepts, advanced techniques, and diverse applications.
Foundation Models: A Survey of Large Language Models
This paper provides a comprehensive overview of large language models, discussing their architectures, training methodologies, and emergent capabilities.
Read PaperThe Prompt Report: A Systematic Survey of Prompting Techniques
Explores various techniques used to design effective prompts for large language models, categorizing them and analyzing their impact on model performance.
Read PaperA Survey on Retrieval-Augmented Text Generation for Large Language Models
Delves into Retrieval-Augmented Generation (RAG) methods, which combine LLMs with external knowledge bases to improve factual accuracy and reduce hallucinations.
Read PaperA Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
Addresses the phenomenon of hallucinations in LLMs, providing a taxonomy of types, underlying causes, and current mitigation strategies.
Read PaperParameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
Reviews various Parameter-Efficient Fine-Tuning (PEFT) techniques that enable adaptation of large pre-trained models to downstream tasks with minimal computational cost.
Read PaperA Survey on Large Language Model based Autonomous Agents
Examines the emerging field of LLM-based autonomous agents, discussing their architectures, capabilities, and potential applications in complex environments.
Read PaperA Survey on Multimodal Large Language Models
Provides an overview of multimodal LLMs, which integrate and process information from multiple modalities such as text, images, and audio.
Read PaperA Survey on Evaluation of Large Language Models
Discusses various methodologies and metrics used to evaluate the performance, capabilities, and limitations of large language models across different tasks.
Read PaperSurvey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks
Investigates the security vulnerabilities of LLMs, focusing on adversarial attacks and their implications for model robustness and trustworthiness.
Read PaperLarge Language Model Alignment: A Survey
Explores the critical area of LLM alignment, covering techniques and challenges in ensuring models behave in accordance with human values and intentions.
Read PaperReasoning with Large Language Models: A Survey
Focuses on the reasoning capabilities of LLMs, reviewing different approaches to enhance their logical and problem-solving skills.
Read PaperEfficient Large Language Models: A Survey
Examines techniques for improving the efficiency of LLMs in terms of computational cost, memory usage, and inference speed.
Read PaperExplainability for Large Language Models: A Survey
Covers methods and challenges in making the decisions and internal workings of large language models more transparent and understandable to humans.
Read PaperTrustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Provides a comprehensive overview of trustworthiness in LLMs, including aspects like safety, fairness, robustness, and interpretability.
Read PaperLarge Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications
Surveys the application of LLMs in code generation, discussing challenges, techniques, evaluation metrics, and real-world use cases.
Read PaperA Survey of Large Language Models in Medicine: Progress, Application, and Challenge
Explores the growing role of LLMs in the medical domain, highlighting their applications, current progress, and the unique challenges faced.
Read PaperA Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias
Examines the development and challenges of multilingual LLMs, focusing on data corpora, cross-lingual alignment, and potential biases.
Read Paper