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A Reality check of the benefits of LLM in business

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arxiv 2406.10249 v1 pith:RQB7ZZZJ submitted 2024-06-09 cs.AI cs.CL

classification cs.AIcs.CL
keywords llmsbusinesslanguagelimitationsmodelsreadinessreal-worldunderstanding
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks by leveraging vast amounts of online texts. Unlike conventional models, LLMs can adapt to new domains through prompt engineering without the need for retraining, making them suitable for various business functions, such as strategic planning, project implementation, and data-driven decision-making. However, their limitations in terms of bias, contextual understanding, and sensitivity to prompts raise concerns about their readiness for real-world applications. This paper thoroughly examines the usefulness and readiness of LLMs for business processes. The limitations and capacities of LLMs are evaluated through experiments conducted on four accessible LLMs using real-world data. The findings have significant implications for organizations seeking to leverage generative AI and provide valuable insights into future research directions. To the best of our knowledge, this represents the first quantified study of LLMs applied to core business operations and challenges.

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Cited by 1 Pith paper

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

  1. Towards Inclusive NLP: Assessing Compressed Multilingual Transformers across Diverse Language Benchmarks

    cs.CL 2025-07 reject novelty 3.0 of 10

    Across Arabic, English, and Kannada benchmarks, 4-bit and 8-bit quantization preserves most accuracy while aggressive pruning degrades larger multilingual models more than smaller ones.

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