- Authors: Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, Pranav Sandeep Dulepet, Saurav Vidyadhara, Dayeon Ki, Sweta Agrawal, Chau Pham, Gerson Kroiz, Feileen Li, Hudson Tao, Ashay Srivastava, Hevander Da Costa, Saloni Gupta, Megan L. Rogers, Inna Goncearenco, Giuseppe Sarli, Igor Galynker, Denis Peskoff, Marine Carpuat, Jules White, Shyamal Anadkat, Alexander Hoyle, Philip Resnik
- Source: Excerpts from “2406.06608.pdf”
Introduction
This briefing doc reviews key themes and findings from “The Prompt Report: A Systematic Survey of Prompting Techniques” (Schulhoff et al., 2024). This comprehensive study explores the burgeoning field of prompt engineering, encompassing a wide array of techniques used to elicit desired outputs from Generative AI (GenAI) models, particularly focusing on large language models (LLMs).
1. What is Prompting?
Prompting is the process of providing an input, called a “prompt,” to a GenAI, which then generates a response. Prompts can be textual, such as “Write a poem about trees,” or multimodal, incorporating images, audio, videos, or a combination thereof.
2. Key Prompting Techniques
The study categorizes and analyzes a multitude of text-based prompting techniques. Figure 2.2 in the source document provides a visual overview. Here are some highlights:
- In-Context Learning (ICL): This technique involves providing the LLM with a few examples of
input-output pairs within the prompt. This “demonstration” allows the model to learn from these
examples and generalize to new, unseen instances.
- Key Factors in ICL: Exemplar quantity, similarity to the test instance, label distribution, and format significantly influence the effectiveness of ICL. (See Figure 2.4 for examples)
- Thought Generation: Prompting techniques that encourage the LLM to articulate its reasoning process before providing the final answer. Popular examples include:
- Chain-of-Thought (CoT): Explicitly prompting the model to generate a step-by-step reasoning chain
leading to the answer.
- Zero-Shot CoT: Utilizing prompts like “Let’s think step-by-step” to induce CoT reasoning without providing explicit examples.
- Few-Shot CoT: Providing a few examples of questions with accompanying reasoning chains to guide the model.
- Decomposition: Breaking down complex problems into smaller, more manageable sub-problems that the LLM can solve individually before combining the results.
- Self-Criticism: Techniques that encourage LLMs to evaluate and refine their own outputs, leading
to improved accuracy and reliability. For instance:
- Self-Calibration: The LLM is prompted to assess the correctness of its own answer, providing a measure of confidence in the generated output.
3. Prompt Engineering Process
Prompt engineering is the systematic process of crafting prompts to optimize GenAI output. The process typically involves iterative refinement, as depicted in Figure 1.4:
- Dataset Inference: Applying the current prompt template to a dataset and observing the model’s responses.
- Performance Evaluation: Assessing the quality of the model’s outputs based on a chosen metric.
- Prompt Template Modification: Adjusting the prompt template based on the evaluation results to improve performance.
4. Beyond Text: Multimodal and Agent-Based Prompting
The study extends the discussion beyond text-based prompts to cover multimodal and agent-based approaches:
- Multimodal Prompting: Explores techniques that leverage multiple modalities, such as images or audio, in conjunction with text to elicit richer and more contextually grounded responses. (See Figure 3.2 for categories.)
- Agent-Based Prompting: Examines techniques that enable LLMs to interact with external tools or
environments, effectively transforming them into agents capable of complex, goal-directed
behaviors.
- Tool Use Agents: LLMs given access to tools like calculators or web search engines.
- Code-Generation Agents: LLMs that generate code to execute in programming environments.
- Retrieval Augmented Generation (RAG): Agents that retrieve relevant information from external knowledge bases to enhance their responses.
5. Key Challenges and Issues
Prompt engineering, while promising, faces challenges:
- Security: Prompt injection and jailbreaking attacks pose significant threats, potentially compromising the integrity and safety of LLM applications.
- Alignment: Ensuring that LLM outputs align with human values and expectations is crucial. Issues like prompt sensitivity, overconfidence, biases, stereotypes, and ambiguity need careful consideration.
6. Benchmarking and Case Studies
The study presents benchmarking experiments and a detailed case study to illustrate the practical application and challenges of prompt engineering.
- Benchmarking: Evaluating various prompting techniques across different datasets, such as MMLU, reveals that even subtle changes in prompt phrasing or format can significantly impact performance. (See Figures 6.1 and 6.5 for examples)
- Case Study: A detailed exploration of prompt engineering for an entrapment detection task highlights the iterative nature of the process, the impact of model choice, and the potential of techniques like AutoDiCoT. The case study underscores the difficulty in achieving consistent improvements and the need for creative problem-solving in prompt development. (See Figure 6.6 for the process visualization)
7. Future Directions
The study concludes by highlighting future research avenues in prompt engineering, emphasizing the need for standardized evaluation methodologies, the development of robust tools for prompt creation and optimization, and addressing the ethical and security considerations inherent in this rapidly evolving field.
Quote Highlights:
The ability to prompt models, particularly prompting with natural language, makes them easy to interact with and use flexibly across a wide range of use cases.
When creating GenAI systems, it can be useful to have LLMs criticize their own outputs… This could simply be a judgement… or the LLM could be prompted to provide feedback, which is then used to improve the answer.
A take-away from this initial phase is that the “guard rails” associated with some large language models may interfere with the ability to make progress on a prompting task, and this could influence the choice of model for reasons other than the LLM’s potential quality.
This briefing doc provides a high-level overview of the key themes and findings in the source document. For a deeper understanding, please refer to the original document for further details and specific examples.