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LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey

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arxiv 2402.14558 v2 pith:7ZJMYR6R submitted 2024-02-22 cs.CL

classification cs.CL
keywords llmsindustrialacrosschallengesindustrylanguagequestionssurvey
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions. We maintain the Github repository with the most recent papers in the field.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know"

    cs.CL 2026-02 conditional novelty 6.0 of 10

    Disagreement between direct and decomposed prompting is a training-free error signal that outperforms standard uncertainty baselines for closed-book QA abstention.

  2. Agent Ideate: A Framework for Product Idea Generation from Patents Using Agentic AI

    cs.AI 2025-07 conditional novelty 4.0 of 10

    A multi-agent LLM framework with optional web search generates product ideas from patents and outperforms a single-prompt LLM on 150 patents, though results vary by domain.

  3. Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives

    cs.CL 2025-02 conditional novelty 4.0 of 10

    The paper proposes that asymptotic analysis with LLM primitives, treating one forward pass as the cost unit, is the right framework for scaling multi-agent LLM systems.

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