Despite not being explicitly trained for this purpose, models like Mistral and LLaMA have demonstrated impressive results across numerous tasks, including generating solutions to Mathematical Word Problems (MWPs). A MWP involves translating a textual description into a mathematical model or equation that solving it. However, these models face challenges in accurately interpreting and utilizing the numerical information present in the MWP statements, which can lead to errors in the generated solutions. To better understand the limitations of LLMs, we analyzed the MWP where models failed to accurately solve problems from the SVAMP dataset. By categorizing these MWPs, we identify specific types of problems where the models are most prone to errors, providing insights into the underlying challenges faced by LLMs in problem-solving scenarios and open new modeling opportunities. By understanding the expected errors, researchers can design strategies to adequately model problems more effectively and choose the most suitable LLM for solving them taking into account each model’s strengths and weaknesses.

Robots are often deployed in remote locations for tasks such as exploration, where users cannot directly perceive the agent and its environment. For Human-In-The-Loop applications, operators must have a comprehensive understanding of the robot’s current state and its environment to take necessary actions and effectively assist the agent. In this work, we compare different explanation styles to determine the most effective way to convey real-time updates to users. Additionally, we formulate these explanation styles as separate fine-tuning tasks and assess the effectiveness of large language models in delivering in-mission updates to maintain situation awareness. The code and dataset for this work are available at:———