mlsanityData to 7 Oct 2026

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

arXiv 2402.14558 ↗First posted 22 Feb 2024cs.CLcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 65 of its 116 references that are in the map. First posted on arXiv 22 Feb 2024.

Builds on

65 of 116 references.

References · 65

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2023GPT-4 Technical Reportcited by 18,132
  5. 2022Training language models to follow instructions with human feedbackcited by 15,712
  6. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  7. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  8. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  9. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  10. 2021Program Synthesis with Large Language Modelscited by 3,509
  11. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  12. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
38 more
  1. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  2. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 755
  3. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  4. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  5. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
  6. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 394
  7. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 347
  8. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
  9. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 284
  10. 2022Reasoning with Language Model Prompting: A Surveycited by 251
  11. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  12. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matterscited by 221
  13. 2021CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Taskscited by 199
  14. 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 196
  15. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 177
  16. 2022Multi-lingual Evaluation of Code Generation Modelscited by 158
  17. 2023CodePlan: Repository-level Coding using LLMs and Planningcited by 139
  18. 2022Large Language Models Meet NL2Code: A Surveycited by 135
  19. 2023Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluationcited by 134
  20. 2020Logical Natural Language Generation from Open-Domain Tablescited by 90
  21. 2023Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Datasetcited by 90
  22. 2023Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidancecited by 85
  23. 2021What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformerscited by 75
  24. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  25. 2023Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermarkcited by 61
  26. 2023Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Taskscited by 59
  27. 2023The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Valuescited by 53
  28. 2023On the steerability of large language models toward data-driven personascited by 53
  29. 2023A Practical Survey on Zero-shot Prompt Design for In-context Learningcited by 49
  30. 2023Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMscited by 49
  31. 2023ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Applicationcited by 48
  32. 2023Training Data Extraction From Pre-trained Language Models: A Surveycited by 43
  33. 2023Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answeringcited by 41
  34. 2023Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curationcited by 38
  35. 2023Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutorscited by 38
  36. 2023Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspectivecited by 34
  37. 2023DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarioscited by 33
  38. 2023Exploring the Boundaries of GPT-4 in Radiologycited by 31

50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 4.

12 papers.

  1. 2025Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitivescited by 7
  2. 2025Training-Free Policy Violation Detection via Activation-Space Whitening in LLMscited by 6
  3. 2025Position: Foundation Models for Tabular Data within Systemic Contexts Need Groundingcited by 5
  4. 2025No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Modelscited by 3
  5. 2025Agent Ideate: A Framework for Product Idea Generation from Patents Using Agentic AIcited by 3
  6. 2025DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversationscited by 1
  7. 2026Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know"cited by 1
  8. 2025AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applicationscited by 0
  9. 2025HATS: High-Accuracy Triple-Set Watermarking for Large Language Modelscited by 0
  10. 2026Speculative Sampling with Reinforcement Learningcited by 0
  11. 2026Is my model perplexed for the right reason? Contrasting LLMs' Benchmark Behavior with Token-Level Perplexitycited by 0
  12. 2026A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operationscited by 0