Large Language Models for Natural Language Processing: Opportunities and Limitations

Abstract

Large Language Models (LLMs) have fundamentally transformed natural language processing (NLP) by demonstrating emergent capabilities in reasoning, generation, multimodal synthesis, and agentic behavior through massive scaling, advanced architectures such as Mixture-of-Experts (MoE), and novel training paradigms including reinforcement learning for reasoning (e.g., DeepSeek-R1 and OpenAI o-series). This paper traces the historical evolution of computational linguistics from rule-based systems to Transformer-based foundational models and examines 2026-era advancements, including long-context processing (up to 20 million tokens), reasoning-first deliberation loops, neuro-symbolic integration, and domain-specific agentic applications in software engineering, healthcare, biomedicine, and jurisprudence. While LLMs deliver substantial operational opportunities such as 4× faster bug resolution, 50–80% reduction in legal research time, and accelerated drug discovery they continue to grapple with persistent limitations, including hallucinations (still ~6% in frontier models), adversarial vulnerabilities, sociocultural biases, position bias in long contexts, and environmental sustainability concerns amid exascale infrastructure projects like Stargate and the Genesis Mission. The analysis contrasts long-context models with Retrieval-Augmented Generation (RAG) approaches and highlights promising directions in neuro-symbolic AI to bridge reasoning gaps toward Artificial General Intelligence (AGI). Ultimately, this survey underscores the need for balanced integration of scaling, efficiency, safety, and ethical alignment to realize LLMs' full potential while mitigating risks in high-stakes real-world deployment.