# A Survey of Robot Intelligence with Large Language Models
## 3 Related Works
### Prompt Techniques for Increasing LLM Performance  
To enhance the performance of LLMs, the most straightforward approach involves training with additional data via fine-tuning techniques, which mirrors supervised learning in conventional machine learning. Another method for improving performance involves the use of in-context learning, which capitalizes on prompts for zero-shot learning, a capability first observed in LLMs with the advent of GPT-3. The adaptation of these prompts for specific tasks is known as prompt engineering. Fundamentally, prompt engineering (or prompting) entails supplying inputs to the model to perform a distinct task, designing the input format to encapsulate the task's purpose and context, and generating the desired output. The four components of prompt engineering can be analyzed as follows: within the prompt, \"_Instructions_\" delineate the specific tasks or directives for the model and  
Figure 3: An overview of four strategies for parameter-efficient fine-tuning: (**a**) Adapter Tuning, (**b**) Prefix Tuning, (**c**) Prompt Tuning, and (**d**) Low-Rank Adaptation [5]. \"Context\"_ provides external or additional contextual information that can tune the model. Furthermore, \"Input data\" refers to the type of input or questions seeking answers, and \"Output data\" defines the output type or format within the prompt, thereby optimizing the LLM's performance for particular tasks. Various methodologies for creating prompts have been introduced, as described below. **Zero-shot prompting**[53] is a technique that allows the model to take on new tasks with no prior examples. The model relies solely on the task description or instructions without additional training. Likewise, **few-shot prompting**[49] introduces a small number of examples to aid the model in learning new tasks. This approach does not require extensive datasets and can improve the model's performance through a limited set of examples. **Chain-of-thought** (CoT) [41] is a technique that explicitly describes intermediate reasoning steps, enabling the model to perform step-by-step reasoning. This approach allows the model to incrementally solve complex tasks. For instance, when asked, \"If someone's age will be 30 in 5 years, how old are they now?\", the model uses the information \"age in 5 years is 30\" to perform the intermediate reasoning step of \"30 \(-\) 5 = 25\" to derive the final answer. **Self-consistency**[120] involves the model generating various independent reasoning paths through few-Shot CoT, ultimately selecting the most consistent answer among the outputs. This method enhances the performance of CoT prompts in both arithmetic and commonsense reasoning tasks. **Multimodal CoT**[121] is a two-stage framework that integrates text and visual modalities. Initially, intermediate reasoning steps are generated through rationale generation based on multimodal data. Subsequently, the answer inferences are intertwined, and the informative rationales are utilized to derive the final answer. Generally, CoT relies on human-generated annotations, which may not always provide the optimal solution for problem-solving. To overcome this limitation, **active prompt**[122] has been proposed. Active prompt enhances model performance by intensively training the model on questions with higher uncertainty levels. It evaluates the uncertainty of answers by posing questions to the model, with or without CoT examples. Questions with high uncertainty are selected for human annotation, and newly annotated examples are used to reason through each question. **Program-aided language models** (PAL) [123] is a technique that employs the model to understand natural language problems and generate programs as intermediate reasoning steps. Unlike CoT, PAL solves problems stepwise using a program runtime such as Python rather than free-form text.