A critical review of methods and challenges in large language models

• URL: http://arxiv.org/abs/2404.11973v2
• Date: Fri, 26 Sep 2025 14:07:34 GMT
• Title: A critical review of methods and challenges in large language models
• Authors: Milad Moradi, Ke Yan, David Colwell, Matthias Samwald, Rhona Asgari,
• Abstract summary: Review provides in-depth analysis of Large Language Models (LLMs)<n>Examines the evolution from Recurrent Neural Networks (RNNs) to Transformer models.<n>Describes state-of-the-art techniques such as in-context learning and various fine-tuning approaches.
• Score: 6.850038413666062
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This critical review provides an in-depth analysis of Large Language Models (LLMs), encompassing their foundational principles, diverse applications, and advanced training methodologies. We critically examine the evolution from Recurrent Neural Networks (RNNs) to Transformer models, highlighting the significant advancements and innovations in LLM architectures. The review explores state-of-the-art techniques such as in-context learning and various fine-tuning approaches, with an emphasis on optimizing parameter efficiency. We also discuss methods for aligning LLMs with human preferences, including reinforcement learning frameworks and human feedback mechanisms. The emerging technique of retrieval-augmented generation, which integrates external knowledge into LLMs, is also evaluated. Additionally, we address the ethical considerations of deploying LLMs, stressing the importance of responsible and mindful application. By identifying current gaps and suggesting future research directions, this review provides a comprehensive and critical overview of the present state and potential advancements in LLMs. This work serves as an insightful guide for researchers and practitioners in artificial intelligence, offering a unified perspective on the strengths, limitations, and future prospects of LLMs.

Related papers

• Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities [62.05713042908654]
  This paper provides a review of advances in Large Language Models (LLMs) alignment through the lens of inverse reinforcement learning (IRL)<n>We highlight the necessity of constructing neural reward models from human data and discuss the formal and practical implications of this paradigm shift.
  arXiv  Detail & Related papers  (2025-07-17T14:22:24Z)
• Large Language Models in Argument Mining: A Survey [15.041650203089057]
  Argument Mining (AM) focuses on extracting argumentative structures from text.<n>The advent of Large Language Models (LLMs) has profoundly transformed AM, enabling advanced in-context learning.<n>This survey systematically synthesizes recent advancements in LLM-driven AM.
  arXiv  Detail & Related papers  (2025-06-19T15:12:58Z)
• LLM Inference Enhanced by External Knowledge: A Survey [16.319049759753106]
  This study explores strategies for using external knowledge to enhance large language models (LLMs)<n>Our comparative analysis highlights the trade-offs among interpretability, scalability, and performance.
  arXiv  Detail & Related papers  (2025-05-30T09:08:51Z)
• A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI [0.0]
  Large-language models (LLMs) can help students, teachers, and peers understand "where learners are going," "where learners currently are," and "how to move learners forward"<n>This review provides a comprehensive foundation for integrating LLMs into formative assessment in a pedagogically informed manner.
  arXiv  Detail & Related papers  (2025-05-29T12:52:43Z)
• LLM Post-Training: A Deep Dive into Reasoning Large Language Models [131.10969986056]
  Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications. Post-training methods enable LLMs to refine their knowledge, improve reasoning, enhance factual accuracy, and align more effectively with user intents and ethical considerations.
  arXiv  Detail & Related papers  (2025-02-28T18:59:54Z)
• Large Language Model for Qualitative Research -- A Systematic Mapping Study [3.302912592091359]
  Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools. This study systematically maps the literature on the use of LLMs for qualitative research. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes.
  arXiv  Detail & Related papers  (2024-11-18T21:28:00Z)
• Surveying the MLLM Landscape: A Meta-Review of Current Surveys [17.372501468675303]
  Multimodal Large Language Models (MLLMs) have become a transformative force in the field of artificial intelligence. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs.
  arXiv  Detail & Related papers  (2024-09-17T14:35:38Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning [0.9110413356918055]
  This research pioneers the use of fine-tuned Large Language Models (LLMs) to automate Systematic Literature Reviews ( SLRs) Our study employed the latest fine-tuning methodologies together with open-sourced LLMs, and demonstrated a practical and efficient approach to automating the final execution stages of an SLR process. The results maintained high fidelity in factual accuracy in LLM responses, and were validated through the replication of an existing PRISMA-conforming SLR.
  arXiv  Detail & Related papers  (2024-04-08T00:08:29Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)
• A Comprehensive Overview of Large Language Models [68.22178313875618]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. This article provides an overview of the existing literature on a broad range of LLM-related concepts.
  arXiv  Detail & Related papers  (2023-07-12T20:01:52Z)
• Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
  Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI) We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
  arXiv  Detail & Related papers  (2023-07-05T06:03:40Z)
• PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews using Domain-specific Finetuned Large Language Models [0.0]
  This paper proposes an AI-enabled methodological framework that combines the power of Large Language Models (LLMs) with the rigorous reporting guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) By finetuning LLMs on domain-specific academic papers that have been selected as a result of a rigorous SLR process, the proposed PRISMA-DFLLM reporting guidelines offer the potential to achieve greater efficiency, reusability and scalability.
  arXiv  Detail & Related papers  (2023-06-15T02:52:50Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)
• Information Extraction in Low-Resource Scenarios: Survey and Perspective [56.5556523013924]
  Information Extraction seeks to derive structured information from unstructured texts. This paper presents a review of neural approaches to low-resource IE from emphtraditional and emphLLM-based perspectives.
  arXiv  Detail & Related papers  (2022-02-16T13:44:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.