# A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI

arXiv: https://arxiv.org/abs/2307.15425 · HTML: https://mlsanity.com/paper/2307.15425/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021)
- [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://mlsanity.com/paper/2104.08826/) (2021)
- [Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings](https://mlsanity.com/paper/1809.02169/) (2018)
- [Do We Still Need Clinical Language Models?](https://mlsanity.com/paper/2302.08091/) (2023)
- [ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model](https://mlsanity.com/paper/2304.03086/) (2023)
- [One Model for the Learning of Language](https://mlsanity.com/paper/1711.06301/) (2017)
- [Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety](https://mlsanity.com/paper/2212.06295/) (2022)
- [Reprogramming Pretrained Language Models for Protein Sequence Representation Learning](https://mlsanity.com/paper/2301.02120/) (2023)
- [Deep Learning for Technical Document Classification](https://mlsanity.com/paper/2106.14269/) (2021)

## Built on by (most cited first, 7 of 7)

- [Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks](https://mlsanity.com/paper/2409.08087/) (2024)
- [Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals](https://mlsanity.com/paper/2404.13885/) (2024)
- [Natural Language Generation in Healthcare: A Review of Methods and Applications](https://mlsanity.com/paper/2505.04073/) (2025)
- [ConsumerBench: Benchmarking Generative AI Applications on End-User Devices](https://mlsanity.com/paper/2506.17538/) (2025)
- [ChatGPT for automated grading of short answer questions in mechanical ventilation](https://mlsanity.com/paper/2505.04645/) (2025)
- [Exploring Zero-Shot App Review Classification with ChatGPT: Challenges and Potential](https://mlsanity.com/paper/2505.04759/) (2025)
- [Generative AI and the future of scientometrics: current topics and future questions](https://mlsanity.com/paper/2507.00783/) (2025)

## Abstract (arXiv)

This paper examines the comparative effectiveness of a specialized compiled language model and a general-purpose model like OpenAI's GPT-3.5 in detecting SDGs within text data. It presents a critical review of Large Language Models (LLMs), addressing challenges related to bias and sensitivity. The necessity of specialized training for precise, unbiased analysis is underlined. A case study using a company descriptions dataset offers insight into the differences between the GPT-3.5 and the specialized SDG detection model. While GPT-3.5 boasts broader coverage, it may identify SDGs with limited relevance to the companies' activities. In contrast, the specialized model zeroes in on highly pertinent SDGs. The importance of thoughtful model selection is emphasized, taking into account task requirements, cost, complexity, and transparency. Despite the versatility of LLMs, the use of specialized models is suggested for tasks demanding precision and accuracy. The study concludes by encouraging further research to find a balance between the capabilities of LLMs and the need for domain-specific expertise and interpretability.