Large Language Models (LLMs) have demonstrated great performance in Natural Language Processing (NLP) applications. However, they have high computational costs when fine-tuning them, which can lead to incorrect information being generated, i.e., hallucinations. Two viable strategies have been established to solve these problems: parameter-efficient methods such as Low-Rank Adaptation (LoRA) to minimize computing demands and fact-checking to minimize hallucinations. Verifying the accuracy and dependability of LLM results requires careful fact-checking. Fact-checking can detect and lessen the hallucinations that LLMs may cause by comparing text generated by the model with reliable sources. This procedure is especially crucial in fields like journalism, law, and healthcare, where accuracy is vital. Models that undergo fact-checking are better able to retain their credibility, which makes them more appropriate for use in crucial applications. However, […]
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