The volume, variety, and velocity of change in vulnerabilities and exploits have made incident threat analysis challenging with human expertise and experience along. Tactics, Techniques, and Procedures (TTPs) are to describe how and why attackers exploit vulnerabilities. However, a TTP description written by one security professional can be interpreted very differently by another, leading to confusion in cybersecurity operations or even business, policy, and legal decisions. Meanwhile, advancements in AI have led to the increasing use of Natural Language Processing (NLP) algorithms to assist the various tasks in cyber operations. With the rise of Large Language Models (LLMs), NLP tasks have significantly improved because of the LLM's semantic understanding and scalability. This leads us to question how well LLMs can interpret TTPs or general cyberattack descriptions to inform analysts of the intended purposes of cyberattacks. We propose to analyze and compare the direct use of LLMs (e.g., GPT-3.5) versus supervised fine-tuning (SFT) of small-scale-LLMs (e.g., BERT) to study their capabilities in predicting ATT&CK tactics. Our results reveal that the small-scale-LLMs with SFT provide a more focused and clearer differentiation between the ATT&CK tactics (if such differentiation exists). On the other hand, direct use of LLMs offer a broader interpretation of cyberattack techniques. When treating more general cases, despite the power of LLMs, inherent ambiguity exists and limits their predictive power. We then summarize the challenges and recommend research directions on LLMs to treat the inherent ambiguity of TTP descriptions used in various cyber operations.