Demonstration Notebook: Finding the Most Suited In-Context Learning Example from Interactions

Yiming Tang, Bin Dong·June 16, 2024

Summary

The paper introduces a novel approach called the "demonstration notebook" to enhance in-context learning for large language models (LLMs). This method involves automatically selecting and constructing personalized demonstrations based on past interactions, addressing dataset heterogeneity. The notebook consists of a demonstration set, interaction record set, and noted question set, which are iteratively expanded, collected, and pruned. The notebook outperforms existing techniques in reasoning tasks, text summarization, and prompt compression, showing the significance of question-specific demonstrations. The study also introduces the concept of demonstrative regimes, revealing the underlying structure of when and how demonstrations are effective for different question types. The demonstration notebook not only improves LLM performance but also contributes to a deeper understanding of prompt engineering and the role of demonstrations in enhancing AI problem-solving abilities.

Key findings

1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of automatic demonstration generation and selection in the context of in-context learning examples . This problem involves leveraging information from past interactions to construct demonstrations that can assist in solving various questions effectively. The approach proposed in the paper, the "demonstration notebook," is a novel method that simultaneously tackles the tasks of automatic demonstration construction and selection . While the concept of automatic demonstration generation is not entirely new, the specific method introduced in the paper, focusing on heterogeneous demonstration selection and demonstrative regimes, represents a fresh approach to prompt engineering .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that heterogeneous demonstration selection is crucial in prompt engineering, as different demonstrations are effective for various sets of questions, leading to the concept of demonstrative regimes. The study aims to show that demonstrations exhibit distinct demonstrative regimes, often forming low-dimensional manifolds in the embedding space, which can revolutionize retrieval and generation-based prompt engineering methods .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Demonstration Notebook: Finding the Most Suited In-Context Learning Example from Interactions" proposes several novel ideas, methods, and models in the field of in-context learning and prompt engineering:

  1. Demonstration Notebook Algorithm: The paper introduces a novel method called the "demonstration notebook" algorithm, which addresses automatic demonstration generation and selection simultaneously. This algorithm has shown state-of-the-art (SOTA) results in demonstration construction, surpassing other approaches in automatic demonstration generation .

  2. Demonstrative Regimes: The study pioneers in exploring the concept of demonstrative regimes, which characterize the questions that can be solved effectively with the presence of a demonstration. By visualizing these demonstrative regimes, the paper provides valuable insights for intuitive understanding and use of in-context learning examples .

  3. Distance in Embedding Space: The paper highlights that different demonstrations are effective for heterogeneous sets of questions and introduces the concept that demonstrative regimes are often in the form of low-dimensional manifolds in the embedding space. This finding suggests that distance in the embedding space may not be a good heuristic for context selection, indicating that learning-based retrievers could be more suitable for context selection in prompt engineering compared to cosine-similarity based retrievers .

  4. On-Policy Collection: The paper describes an on-policy collection procedure for efficiently collecting interaction records of the demonstration notebook. This procedure involves training a prompter based on the current interaction record set to guide demonstration selection. The prompter assigns scores to demonstrations, selects the top-k demonstrations, and presents them to the language model for improved performance .

  5. Automatic Demonstration Construction Methods: The study discusses various automatic demonstration construction methods like AutoCoT and PromptSO. AutoCoT utilizes k-means clustering to select demonstrative questions and generate demonstrations automatically, while PromptSO employs principal component analysis for question selection before automatic demonstration generation. These methods aim to enhance the language model's output but may overlook the heterogeneity of questions within a dataset, which the demonstration notebook algorithm addresses by providing question-specific demonstrations . The "Demonstration Notebook" method proposed in the paper introduces several key characteristics and advantages compared to previous methods in the field of prompt engineering and in-context learning:

  6. Novel Prompt Engineering Method: The Demonstration Notebook method is a novel prompt engineering approach that addresses the tasks of automatic demonstration construction and selection simultaneously . This method stands out by offering a comprehensive strategy consisting of four procedures iterated for several epochs, including demonstration expansion, on-policy collection, off-policy collection, and pruning .

  7. Question-Specific Demonstrations: Unlike previous methods that rely on fixed demonstrations neglecting the heterogeneity of questions within a dataset, the Demonstration Notebook emphasizes the importance of question-specific demonstrations. It aims to select appropriate demonstrations tailored to each question, enhancing the effectiveness of in-context learning examples .

  8. Demonstrative Regimes: The paper pioneers in exploring the concept of demonstrative regimes, characterizing the questions that can be effectively solved with the presence of a demonstration. This concept provides valuable insights into how demonstrations relate to different question types within a dataset, promoting more intuitive use of in-context examples .

  9. Visualization and Intuitive Understanding: The Demonstration Notebook method offers rigorous experimental results and visualizations to facilitate a more intuitive understanding of using demonstrations in in-context learning. The visualizations show that demonstrative regimes are often in the form of low-dimensional manifolds in the embedding space, indicating potential advancements in retrieval and prompt engineering methods .

  10. State-of-the-Art Results: Experimental results from the Demonstration Notebook method have consistently outperformed all existing approaches supporting automatic demonstration construction, achieving state-of-the-art results in demonstration construction . This method has shown superiority in beating other approaches in automatic demonstration generation, showcasing its effectiveness and versatility in prompt engineering tasks .

  11. Versatility and Adaptability: The Demonstration Notebook algorithm can be extended to other tasks such as prompt compression and article summarization, demonstrating its versatility and adaptability across different applications . This highlights the method's potential to be applied in various settings beyond in-context learning examples, showcasing its broad utility in the field of natural language processing.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of prompt engineering and in-context learning. Noteworthy researchers in this area include Jason Wei, Xuezhi Wang, Dale Schuurmans, Quoc Le, Ed Huai hsin Chi, Denny Zhou, Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa, Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, Tushar Khot, Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, Zhiting Hu, and many others .

The key to the solution mentioned in the paper involves the use of a novel prompt engineering method called the "demonstration notebook." This method focuses on automatic demonstration construction and selection simultaneously. The demonstration notebook helps identify the most suitable in-context learning example for a question by gathering and reusing information from the large language model's past interactions. This approach has been shown to outperform all existing methods for automatic demonstration construction and selection, achieving state-of-the-art results on several reasoning benchmarks .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the demonstration notebook method across various settings, including reasoning tasks, prompt compression, and article summarization . The experiments focused on testing the performance of the demonstration notebook in comparison to existing methods on reasoning benchmarks such as arithmetic reasoning, commonsense reasoning, and symbolic reasoning . The study also extended its evaluation to include the Meta Llama3(8B) model and OpenAI gpt-3.5-turbo(175B) model, which are representative large language models . The evaluation involved comparing the results of the demonstration notebook method with several baselines, including Zero-shot CoT, Manual CoT, AutoCoT, and PromptSO, to demonstrate the superiority of the proposed approach . The experiments aimed to showcase the effectiveness of the demonstration notebook in automatic demonstration construction and selection, highlighting its versatility and performance in prompt engineering tasks .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a set of nine reasoning benchmarks, including arithmetic reasoning, commonsense reasoning, and symbolic reasoning . The code used in the study is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper introduces a novel method called the demonstration notebook, which focuses on automatic demonstration generation and selection based on past interactions . The experimental results demonstrate that this approach outperforms existing methods in automatic demonstration construction, showcasing its effectiveness in prompt engineering . Additionally, the paper evaluates the method on various reasoning benchmarks, including arithmetic, commonsense, and symbolic reasoning tasks, showing consistent improvement over other approaches . The visualization results from the experiments offer valuable insights into the use of demonstrations in in-context learning, enhancing the intuitive understanding of leveraging demonstrations for improved reasoning .


What are the contributions of this paper?

The paper makes several key contributions:

  • Introducing a novel prompt engineering method called the demonstration notebook, which addresses the tasks of automatic demonstration construction and selection simultaneously .
  • Beating all existing methods in demonstration construction, which is essential in prompt engineering, through extensive experimentation .
  • Providing a rigorous analysis and visualization of demonstration regimes for different demonstrations, enhancing the intuitive use of in-context examples .

What work can be continued in depth?

Further research in the field of prompt engineering and in-context learning can be expanded in several directions:

  • Exploring Different Prompt Designs: Investigating the impact of various prompt designs on the outputs of large language models (LLMs) can provide insights into how to optimize prompt engineering techniques .
  • Enhancing Demonstration Construction: Research can focus on improving the automatic construction of demonstrations tailored to specific questions by leveraging past interactions of LLMs, which can lead to more effective in-context learning examples .
  • Analyzing Demonstrative Regimes: Delving deeper into the concept of demonstrative regimes, which characterize the effectiveness of different demonstrations for heterogeneous sets of questions, can offer valuable insights into how demonstrations relate to specific question types within datasets .
  • Optimizing Demonstration Selection: Developing more advanced methods for selecting demonstrations based on the LLM's past interactions and performance on specific questions can enhance the prompter's ability to choose the most suitable demonstrations for in-context learning .

Tables

1

Introduction
Background
Evolution of in-context learning in LLMs
Challenges with dataset heterogeneity
Objective
Introducing the demonstration notebook concept
Aim to enhance LLM performance and prompt engineering
Method
Data Collection
Automatic Demonstration Selection
Algorithm for personalized demonstration retrieval
Utilizing interaction records
Construction of Demonstration Set
Selection criteria for relevant examples
Iterative expansion process
Data Preprocessing
Handling Dataset Heterogeneity
Adaptation techniques for diverse data
Standardization and normalization
Interaction Record Set and Noted Question Set
Collection and organization of user interactions
Pruning irrelevant or outdated examples
Performance Evaluation
Experimental Setup
Benchmark tasks: reasoning, text summarization, and prompt compression
Comparison with existing techniques
Results and Analysis
Demonstration notebook's superiority in task performance
Impact on LLM problem-solving abilities
Demonstrative Regimes
Identifying Effective Patterns
Analysis of demonstrative effectiveness for different question types
Exploration of when and how to use demonstrations
Structure and Guidelines
Formulation of principles for optimal demonstration usage
Conclusion
Summary of key findings
Implications for future LLM development and prompt engineering
Limitations and future research directions
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
How does the demonstration notebook address dataset heterogeneity in LLMs?
In which areas does the demonstration notebook demonstrate improved performance compared to existing techniques?
What is the primary method introduced in the paper for enhancing in-context learning in large language models?
What are the three main components of the demonstration notebook?

Demonstration Notebook: Finding the Most Suited In-Context Learning Example from Interactions

Yiming Tang, Bin Dong·June 16, 2024

Summary

The paper introduces a novel approach called the "demonstration notebook" to enhance in-context learning for large language models (LLMs). This method involves automatically selecting and constructing personalized demonstrations based on past interactions, addressing dataset heterogeneity. The notebook consists of a demonstration set, interaction record set, and noted question set, which are iteratively expanded, collected, and pruned. The notebook outperforms existing techniques in reasoning tasks, text summarization, and prompt compression, showing the significance of question-specific demonstrations. The study also introduces the concept of demonstrative regimes, revealing the underlying structure of when and how demonstrations are effective for different question types. The demonstration notebook not only improves LLM performance but also contributes to a deeper understanding of prompt engineering and the role of demonstrations in enhancing AI problem-solving abilities.
Mind map
Pruning irrelevant or outdated examples
Collection and organization of user interactions
Standardization and normalization
Adaptation techniques for diverse data
Iterative expansion process
Selection criteria for relevant examples
Utilizing interaction records
Algorithm for personalized demonstration retrieval
Formulation of principles for optimal demonstration usage
Exploration of when and how to use demonstrations
Analysis of demonstrative effectiveness for different question types
Impact on LLM problem-solving abilities
Demonstration notebook's superiority in task performance
Comparison with existing techniques
Benchmark tasks: reasoning, text summarization, and prompt compression
Interaction Record Set and Noted Question Set
Handling Dataset Heterogeneity
Construction of Demonstration Set
Automatic Demonstration Selection
Aim to enhance LLM performance and prompt engineering
Introducing the demonstration notebook concept
Challenges with dataset heterogeneity
Evolution of in-context learning in LLMs
Limitations and future research directions
Implications for future LLM development and prompt engineering
Summary of key findings
Structure and Guidelines
Identifying Effective Patterns
Results and Analysis
Experimental Setup
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Demonstrative Regimes
Performance Evaluation
Method
Introduction
Outline
Introduction
Background
Evolution of in-context learning in LLMs
Challenges with dataset heterogeneity
Objective
Introducing the demonstration notebook concept
Aim to enhance LLM performance and prompt engineering
Method
Data Collection
Automatic Demonstration Selection
Algorithm for personalized demonstration retrieval
Utilizing interaction records
Construction of Demonstration Set
Selection criteria for relevant examples
Iterative expansion process
Data Preprocessing
Handling Dataset Heterogeneity
Adaptation techniques for diverse data
Standardization and normalization
Interaction Record Set and Noted Question Set
Collection and organization of user interactions
Pruning irrelevant or outdated examples
Performance Evaluation
Experimental Setup
Benchmark tasks: reasoning, text summarization, and prompt compression
Comparison with existing techniques
Results and Analysis
Demonstration notebook's superiority in task performance
Impact on LLM problem-solving abilities
Demonstrative Regimes
Identifying Effective Patterns
Analysis of demonstrative effectiveness for different question types
Exploration of when and how to use demonstrations
Structure and Guidelines
Formulation of principles for optimal demonstration usage
Conclusion
Summary of key findings
Implications for future LLM development and prompt engineering
Limitations and future research directions
Key findings
1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of automatic demonstration generation and selection in the context of in-context learning examples . This problem involves leveraging information from past interactions to construct demonstrations that can assist in solving various questions effectively. The approach proposed in the paper, the "demonstration notebook," is a novel method that simultaneously tackles the tasks of automatic demonstration construction and selection . While the concept of automatic demonstration generation is not entirely new, the specific method introduced in the paper, focusing on heterogeneous demonstration selection and demonstrative regimes, represents a fresh approach to prompt engineering .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that heterogeneous demonstration selection is crucial in prompt engineering, as different demonstrations are effective for various sets of questions, leading to the concept of demonstrative regimes. The study aims to show that demonstrations exhibit distinct demonstrative regimes, often forming low-dimensional manifolds in the embedding space, which can revolutionize retrieval and generation-based prompt engineering methods .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Demonstration Notebook: Finding the Most Suited In-Context Learning Example from Interactions" proposes several novel ideas, methods, and models in the field of in-context learning and prompt engineering:

  1. Demonstration Notebook Algorithm: The paper introduces a novel method called the "demonstration notebook" algorithm, which addresses automatic demonstration generation and selection simultaneously. This algorithm has shown state-of-the-art (SOTA) results in demonstration construction, surpassing other approaches in automatic demonstration generation .

  2. Demonstrative Regimes: The study pioneers in exploring the concept of demonstrative regimes, which characterize the questions that can be solved effectively with the presence of a demonstration. By visualizing these demonstrative regimes, the paper provides valuable insights for intuitive understanding and use of in-context learning examples .

  3. Distance in Embedding Space: The paper highlights that different demonstrations are effective for heterogeneous sets of questions and introduces the concept that demonstrative regimes are often in the form of low-dimensional manifolds in the embedding space. This finding suggests that distance in the embedding space may not be a good heuristic for context selection, indicating that learning-based retrievers could be more suitable for context selection in prompt engineering compared to cosine-similarity based retrievers .

  4. On-Policy Collection: The paper describes an on-policy collection procedure for efficiently collecting interaction records of the demonstration notebook. This procedure involves training a prompter based on the current interaction record set to guide demonstration selection. The prompter assigns scores to demonstrations, selects the top-k demonstrations, and presents them to the language model for improved performance .

  5. Automatic Demonstration Construction Methods: The study discusses various automatic demonstration construction methods like AutoCoT and PromptSO. AutoCoT utilizes k-means clustering to select demonstrative questions and generate demonstrations automatically, while PromptSO employs principal component analysis for question selection before automatic demonstration generation. These methods aim to enhance the language model's output but may overlook the heterogeneity of questions within a dataset, which the demonstration notebook algorithm addresses by providing question-specific demonstrations . The "Demonstration Notebook" method proposed in the paper introduces several key characteristics and advantages compared to previous methods in the field of prompt engineering and in-context learning:

  6. Novel Prompt Engineering Method: The Demonstration Notebook method is a novel prompt engineering approach that addresses the tasks of automatic demonstration construction and selection simultaneously . This method stands out by offering a comprehensive strategy consisting of four procedures iterated for several epochs, including demonstration expansion, on-policy collection, off-policy collection, and pruning .

  7. Question-Specific Demonstrations: Unlike previous methods that rely on fixed demonstrations neglecting the heterogeneity of questions within a dataset, the Demonstration Notebook emphasizes the importance of question-specific demonstrations. It aims to select appropriate demonstrations tailored to each question, enhancing the effectiveness of in-context learning examples .

  8. Demonstrative Regimes: The paper pioneers in exploring the concept of demonstrative regimes, characterizing the questions that can be effectively solved with the presence of a demonstration. This concept provides valuable insights into how demonstrations relate to different question types within a dataset, promoting more intuitive use of in-context examples .

  9. Visualization and Intuitive Understanding: The Demonstration Notebook method offers rigorous experimental results and visualizations to facilitate a more intuitive understanding of using demonstrations in in-context learning. The visualizations show that demonstrative regimes are often in the form of low-dimensional manifolds in the embedding space, indicating potential advancements in retrieval and prompt engineering methods .

  10. State-of-the-Art Results: Experimental results from the Demonstration Notebook method have consistently outperformed all existing approaches supporting automatic demonstration construction, achieving state-of-the-art results in demonstration construction . This method has shown superiority in beating other approaches in automatic demonstration generation, showcasing its effectiveness and versatility in prompt engineering tasks .

  11. Versatility and Adaptability: The Demonstration Notebook algorithm can be extended to other tasks such as prompt compression and article summarization, demonstrating its versatility and adaptability across different applications . This highlights the method's potential to be applied in various settings beyond in-context learning examples, showcasing its broad utility in the field of natural language processing.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of prompt engineering and in-context learning. Noteworthy researchers in this area include Jason Wei, Xuezhi Wang, Dale Schuurmans, Quoc Le, Ed Huai hsin Chi, Denny Zhou, Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa, Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, Tushar Khot, Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, Zhiting Hu, and many others .

The key to the solution mentioned in the paper involves the use of a novel prompt engineering method called the "demonstration notebook." This method focuses on automatic demonstration construction and selection simultaneously. The demonstration notebook helps identify the most suitable in-context learning example for a question by gathering and reusing information from the large language model's past interactions. This approach has been shown to outperform all existing methods for automatic demonstration construction and selection, achieving state-of-the-art results on several reasoning benchmarks .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the demonstration notebook method across various settings, including reasoning tasks, prompt compression, and article summarization . The experiments focused on testing the performance of the demonstration notebook in comparison to existing methods on reasoning benchmarks such as arithmetic reasoning, commonsense reasoning, and symbolic reasoning . The study also extended its evaluation to include the Meta Llama3(8B) model and OpenAI gpt-3.5-turbo(175B) model, which are representative large language models . The evaluation involved comparing the results of the demonstration notebook method with several baselines, including Zero-shot CoT, Manual CoT, AutoCoT, and PromptSO, to demonstrate the superiority of the proposed approach . The experiments aimed to showcase the effectiveness of the demonstration notebook in automatic demonstration construction and selection, highlighting its versatility and performance in prompt engineering tasks .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a set of nine reasoning benchmarks, including arithmetic reasoning, commonsense reasoning, and symbolic reasoning . The code used in the study is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper introduces a novel method called the demonstration notebook, which focuses on automatic demonstration generation and selection based on past interactions . The experimental results demonstrate that this approach outperforms existing methods in automatic demonstration construction, showcasing its effectiveness in prompt engineering . Additionally, the paper evaluates the method on various reasoning benchmarks, including arithmetic, commonsense, and symbolic reasoning tasks, showing consistent improvement over other approaches . The visualization results from the experiments offer valuable insights into the use of demonstrations in in-context learning, enhancing the intuitive understanding of leveraging demonstrations for improved reasoning .


What are the contributions of this paper?

The paper makes several key contributions:

  • Introducing a novel prompt engineering method called the demonstration notebook, which addresses the tasks of automatic demonstration construction and selection simultaneously .
  • Beating all existing methods in demonstration construction, which is essential in prompt engineering, through extensive experimentation .
  • Providing a rigorous analysis and visualization of demonstration regimes for different demonstrations, enhancing the intuitive use of in-context examples .

What work can be continued in depth?

Further research in the field of prompt engineering and in-context learning can be expanded in several directions:

  • Exploring Different Prompt Designs: Investigating the impact of various prompt designs on the outputs of large language models (LLMs) can provide insights into how to optimize prompt engineering techniques .
  • Enhancing Demonstration Construction: Research can focus on improving the automatic construction of demonstrations tailored to specific questions by leveraging past interactions of LLMs, which can lead to more effective in-context learning examples .
  • Analyzing Demonstrative Regimes: Delving deeper into the concept of demonstrative regimes, which characterize the effectiveness of different demonstrations for heterogeneous sets of questions, can offer valuable insights into how demonstrations relate to specific question types within datasets .
  • Optimizing Demonstration Selection: Developing more advanced methods for selecting demonstrations based on the LLM's past interactions and performance on specific questions can enhance the prompter's ability to choose the most suitable demonstrations for in-context learning .
Tables
1
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