![]() ![]() Ĭommon types of prompt injection attacks are: This stands in contrast to the intended operation of instruction-following systems, wherein the ML model is intended only to follow trusted instructions (prompts) provided by the ML model's operator. Prompt injection is a family of related computer security exploits carried out by getting a machine learning model (such as an LLM) which was trained to follow human-given instructions to follow instructions provided by a malicious user. Zero-shot CoT prompting increased the likelihood of toxic output on tasks for which models can make inferences about marginalized groups or harmful topics. While CoT reasoning can improve performance on natural language processing tasks, certain drawbacks exist. This allows for better scaling as one no longer needs to prompt engineer specific CoT prompts for each task to get the corresponding boost in performance. It is also possible to elicit similar reasoning and performance gain with zero-shot prompting, which can be as simple as appending to the prompt the words "Let's think step-by-step". The initial proposition of CoT prompting demonstrated few-shot prompting, wherein at least one example of a question paired with proper human-written CoT reasoning is prepended to the prompt. There are two main methods to elicit chain-of-thought reasoning: few-shot prompting and zero-shot prompting. It is possible to fine-tune models on CoT reasoning datasets to enhance this capability further and stimulate better interpretability. ĬoT prompting is an emergent property of model scale, meaning it works better with larger and more powerful language models. When applied to PaLM, a 540B parameter language model, CoT prompting significantly aided the model, allowing it to perform comparably with task-specific fine-tuned models on several tasks, even setting a new state of the art at the time on the GSM8K mathematical reasoning benchmark. The answer is 9.” Ĭhain-of-thought prompting improves the performance of LLMs on average on both arithmetic and commonsense tasks in comparison to standard prompting methods. They bought 6 more apples, so they have 3 + 6 = 9. If they used 20 to make lunch and bought 6 more, how many apples do they have?”, a CoT prompt might induce the LLM to answer with steps of reasoning that mimic a train of thought like “A: The cafeteria had 23 apples originally. įor example, given the question “Q: The cafeteria had 23 apples. To address this challenge, CoT prompting prompts the model to produce intermediate reasoning steps before giving the final answer to a multi-step problem. While LLMs show impressive performance on various natural language tasks, they still face difficulties with some reasoning tasks that require logical thinking and multiple steps to solve, such as arithmetic or commonsense reasoning questions. LLMs that are trained on large amounts of text using deep learning methods can generate output that resembles human-generated text. The technique was first proposed by Google researchers in 2022. Chain-of-thought Ĭhain-of-thought prompting (CoT) improves the reasoning ability of LLMs by prompting them to generate a series of intermediate steps that lead to the final answer of a multi-step problem. Prompt engineering may work from a large language model (LLM), that is "frozen" (in the sense that it is pretrained), where only the representation of the prompt is learned (in other words, optimized), using methods such as "prefix-tuning" or "prompt tuning". This section needs expansion with: more details from the sources. ![]()
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