## 2 Related Work
### LLMs and Fine-Tuning  
[PERSON] et al. [8] investigate the effects of supervised fine-tuning of large language models (LLMs) on the introduction of new factual knowledge. They explore whether such fine-tuning leads to an increase in the generation of factually incorrect responses--a phenomenon known as hallucination. The study reveals that LLMs tend to acquire new facts at a slower rate during fine-tuning, compared to facts that align with their pre-existing knowledge. Moreover, the findings indicate a linear relationship between the amount of new knowledge in the fine-tuning examples and the propensity of the model to hallucinate, highlighting the challenges of integrating new factual content into pre-trained models. The research underscores the importance of how LLMs are typically better at refining and utilizing their pre-existing knowledge rather than acquiring new information through fine-tuning. This has practical implications for the development and training strategies of LLMs, especially in ensuring that they remain reliable and factual in their outputs. It supports the notion that more controlled and carefully designed fine-tuning processes are required to enhance model reliability without compromising the accuracy and truthfulness of the content generated. [PERSON] et al. [9] propose a novel framework for enhancing the capabilities of pre-trained LLMs for time-series forecasting. Recognizing the inherent limitations of LLMs when applied to non-linguistic data, the study introduces a two-stage fine-tuning approach that adapts these models to the intricacies of time-series data. This includes a time-series alignment stage to familiarize the model with the time-series context, followed by a forecasting fine-tuning stage for specific forecasting tasks. Moreover, their method incorporates a unique two-level aggregation strategy to handle multi-scale temporal information effectively. The LLM4 TS framework demonstrates superior performance across several datasets, outperforming state-of-the-art methods and offering significant improvements in few-shot scenarios, thus highlighting the potential of LLMs in domains beyond their initial linguistic training. This approach not only extends the applicability of LLMs but also enhances their efficiency and accuracy in handling complex time-series forecasting tasks. [PERSON] [10] in his study delves into the nuances of employing LLMs specifically tailored for the financial sector. This research highlights the critical importance of dataset selection, preprocessing, and model choice, which are pivotal for fine-tuning LLMs to effectively address the unique challenges of financial data. By constructing domain-specific vocabularies and adhering to security and regulatory compliance, the study enhances the practical application of LLMs in financial services. Various financial scenarios, including stock price prediction, sentiment analysis of financial news, and automated document processing, illustrate the potential of fine-tuned LLMs to transform traditional financial operations into more efficient and insightful practices. Through detailed experimentation and analysis, [PERSON] contributes to the broader understanding of fine-tuning methodologies and their implications for the advancement of natural language processing technology within the business sector, specifically in finance. [PERSON] et al. [11] discuss the methodology and impact of fine-tuning LLMs for domain-specific applications, particularly within the medical field. Their comprehensive review outlines the general steps and methodological approaches essential for effectively adapting pre-trained LLMs to specialized tasks. It highlights the use of models like Chat-GPT and others for fine-tuning processes that significantly enhance their applicability in specific domains such as medical subspecialties.