Fine-Tuning vs. Self-Instruction: A Comparison of Language Models’ Reasoning Abilities in Multistep Tasks

As language models (LMs) continue to advance, they have the potential to revolutionize various industries, including voice interaction (Vi) and Internet of Things (IoT). However, LMs lack contextual understanding, making it challenging to apply them to real-world scenarios. To address this issue, researchers propose representative VIoT tools that can guide LMs through in-context prompts or fine-tune them for specific applications.
One approach is to provide concise explanations and demonstrations through visual programming techniques, such as Visual Programming [21] and Visual ChatGPT [62]. These works rely on powerful LLMs like ChatGPT, which can be trained on vast datasets to generate accurate responses. However, fine-tuning these models for specific tools requires deep knowledge of the application domains and tool usages.
Another approach is to develop autoencoders and LSTM-based traffic flow prediction methods [60], which can learn from large datasets without relying on strong LLMs. These models can make correct decisions using contextual information but may struggle to query videos in the datasets or complete the entire process.
In summary, VIoT tools aim to bridge the gap between LMs and real-world scenarios by offering concise explanations and demonstrations or fine-tuning these models for specific applications. These tools can help LMs understand contextual information and make accurate decisions without relying on external knowledge. By leveraging these techniques, researchers can unlock the full potential of LMs in various industries, transforming the way we interact with technology and making it more accessible to everyone.