Exploring the landscape of large language models: Foundations, techniques, and challenges

• URL: http://arxiv.org/abs/2404.11973v1
• Date: Thu, 18 Apr 2024 08:01:20 GMT
• Title: Exploring the landscape of large language models: Foundations, techniques, and challenges
• Authors: Milad Moradi, Ke Yan, David Colwell, Matthias Samwald, Rhona Asgari,
• Abstract summary: The article sheds light on the mechanics of in-context learning and a spectrum of fine-tuning approaches. It explores how LLMs can be more closely aligned with human preferences through innovative reinforcement learning frameworks. The ethical dimensions of LLM deployment are discussed, underscoring the need for mindful and responsible application.
• Score: 8.042562891309414
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this review paper, we delve into the realm of Large Language Models (LLMs), covering their foundational principles, diverse applications, and nuanced training processes. The article sheds light on the mechanics of in-context learning and a spectrum of fine-tuning approaches, with a special focus on methods that optimize efficiency in parameter usage. Additionally, it explores how LLMs can be more closely aligned with human preferences through innovative reinforcement learning frameworks and other novel methods that incorporate human feedback. The article also examines the emerging technique of retrieval augmented generation, integrating external knowledge into LLMs. The ethical dimensions of LLM deployment are discussed, underscoring the need for mindful and responsible application. Concluding with a perspective on future research trajectories, this review offers a succinct yet comprehensive overview of the current state and emerging trends in the evolving landscape of LLMs, serving as an insightful guide for both researchers and practitioners in artificial intelligence.

Related papers

• 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)
• 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.