Eliciting Problem Specifications via Large Language Models

Robert E. Wray, James R. Kirk, John E. Laird·May 20, 2024

Summary

This paper explores the use of large language models (LLMs) in automating problem-solving by translating natural language descriptions into semi-formal specifications for cognitive systems. The authors design an LLM-enabled cognitive task analyst agent that generates problem-space definitions and strategies from AI literature. The goal is to reduce human intervention in problem formulation and enable AI systems to tackle diverse problems using domain-general methods. Key points include: 1. LLMs as a tool for automating problem representation, potentially speeding up AI research. 2. Problem spaces and cognitive task analysis (CTA) frameworks, like GOMS, are used to structure problem-solving processes. 3. The CTA agent generates problem specifications adhering to Polya's problem-solving principles and Newell's definition of problem spaces. 4. The agent aims to create problem formulations for weak methods, allowing for autonomous problem-solving. 5. Two alternative system designs are discussed, one relying heavily on LLMs and another with more human intervention. The study highlights the potential of LLMs in streamlining AI development and suggests future directions for multi-modal inputs and more sophisticated problem-solving capabilities. However, it also acknowledges the need for refining problem representations and addressing limitations in current models.

Key findings

5

Paper digest

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

The paper aims to address the challenge of automatically producing formal specifications of problem spaces and problems using Large Language Models (LLMs) when presented with natural language problem descriptions . This problem is not entirely new, as the paper builds on previous work in knowledge-based systems, engineering psychology, and machine learning that have systematized and codified translation processes from problems to their representations in AI systems . The focus is on leveraging LLMs to automate the translation process and enable the immediate application of weak methods for problem-solving tasks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that a significant portion of knowledge creation for certain cognitive-systems applications can be automated through an automated problem specification approach, which has the potential to reduce the need for human mediation in agent knowledge, leading to faster and less mediated development of future cognitive systems .


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

The paper "Eliciting Problem Specifications via Large Language Models" proposes several new ideas, methods, and models in the field of cognitive-systems applications and problem-solving . Here are some key points from the paper:

  1. Generation for novel problems: The paper discusses the assessment of the feasibility of a Cognitive Task Analysis (CTA) Agent to formulate problem spaces for new problems not in its training set. It explores the ability of the Large Language Models (LLMs) to generate unique problem analyses while avoiding the tendency to reproduce similar problems and solutions from its training data .

  2. Distinct personas for LLM agents: The paper suggests developing distinct personas for LLM agents to enhance performance. It mentions examples like STORM, a system with separate editor and expert agents, to improve text generation comparable to Wikipedia pages. Implementing different roles like quality assurance (QA) engineer within the CTA Agent is also considered .

  3. Integrated vs. distinct analytic strategies: The paper questions whether a single analytic strategy is sufficient for different classes of problems or if distinct strategies are needed. It explores the overlap in defining problem spaces for various types of problems and the effectiveness of a unified analytic approach .

  4. Automated problem specification: The paper highlights the potential of automating problem specification processes using LLMs, reducing the need for human mediation in agent knowledge creation. This automation could lead to faster development of cognitive systems and open up new research directions in cognitive-systems research .

These ideas and approaches outlined in the paper aim to advance the field of cognitive-systems applications by leveraging Large Language Models for problem specification and analysis, offering new perspectives on problem-solving methodologies and automation possibilities . The paper "Eliciting Problem Specifications via Large Language Models" introduces novel approaches and models for problem specification, offering distinct characteristics and advantages compared to previous methods . Here are the key characteristics and advantages highlighted in the paper:

  1. Automated Problem Specification: The paper emphasizes the potential of automating problem specification processes using Large Language Models (LLMs). This automation reduces the need for human intervention in agent knowledge creation, leading to faster development of cognitive systems and offering new research directions in cognitive-systems applications .

  2. Distinct Personas for LLM Agents: The paper suggests developing distinct personas for LLM agents to enhance performance. By implementing separate roles like quality assurance (QA) engineer within the Cognitive Task Analysis (CTA) Agent, the system can improve text generation and problem analysis, similar to the STORM system that uses separate editor and expert agents .

  3. Integrated vs. Distinct Analytic Strategies: The paper explores the question of whether a single analytic strategy is sufficient for different classes of problems or if distinct strategies are needed. It investigates the overlap in defining problem spaces for various types of problems and the effectiveness of a unified analytic approach, aiming to optimize problem-solving methodologies .

  4. Generation for Novel Problems: The paper assesses the feasibility of LLMs to formulate problem spaces for new and unfamiliar problems not in their training set. It focuses on generating unique problem analyses while avoiding the tendency to reproduce similar problems and solutions from the training data, showcasing the capability of LLMs in handling novel problem scenarios .

  5. Efficient Knowledge Creation: The automated problem specification approach presented in the paper has the potential to streamline knowledge creation for cognitive-systems applications, reducing the labor-intensive nature of system development and enhancing the robustness and capabilities of cognitive systems. This approach also addresses skepticism from researchers outside the community regarding the actual capabilities of cognitive systems .

Overall, the characteristics and advantages of the proposed methods in the paper offer innovative solutions for problem specification, automation of cognitive-systems development, and improved performance through distinct personas and analytic strategies .


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 works exist in the field of problem specifications using large language models. Noteworthy researchers in this area include R.E. Wray, J. Kirk, and J.E. Laird, who have contributed to the study of problem-solving using cognitive systems and large language models . The key to the solution mentioned in the paper involves a search from the initial state to the goal state using specified operators, aiming to identify unproductive paths and undesirable states to efficiently solve the problem .


How were the experiments in the paper designed?

The experiments in the paper were designed to explore the feasibility of using agentic workflows with large language models to create problem space specifications for knowledge-lean search in a problem-solving architecture like Soar . The experiments involved running variations with the CTA Agent using GPT3.5 and GPT4, presenting the models with problem instances directly, and comparing the results . The experiments aimed to provide precise and correct problem-space specifications to enable successful search in Soar, with a focus on refining analysis and identifying search control knowledge .


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

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context . Additionally, there is no information provided regarding the open-source status of the code used in the research. For specific details on the dataset and code used for quantitative evaluation, further information or clarification from the source document may be required.


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 substantial support for the scientific hypotheses that needed verification. The feasibility evaluation conducted using the CTA Agent with GPT3.5 and GPT4 models, along with a one-shot problem-space formulation, demonstrated the effectiveness of the models in problem-solving tasks . The comparison between GPT3.5 and GPT4 showed that GPT4 excelled in generating formal descriptions of the problem space, indicating an improvement in performance . Additionally, the detailed analysis of the sensitivity of the models and the comparison of their outcomes highlighted the reliability and precision of the solutions generated by the models .

Moreover, the experiments included test cases such as F(4, 9) → 6, which aimed to deliver a specific amount of water using containers without graduated markings, showcasing the models' ability to solve complex problems efficiently . The results of the experiments, including the search states explored and the failure detection rates, indicated the models' reliability in finding solutions even for challenging problems . The experiments also emphasized the importance of search control knowledge in optimizing the problem-solving process .

In summary, the experiments and results presented in the paper offer strong empirical evidence supporting the effectiveness and reliability of the large language models in problem specification tasks. The detailed analysis of the models' performance, sensitivity, and search control knowledge provides valuable insights into their capabilities and highlights their potential for various applications in problem-solving domains .


What are the contributions of this paper?

The paper makes several contributions:

  • It provides a detailed analysis of the initial set of operators, highlighting the need for minor adjustments and clarifications, especially in defining post-conditions for transfer operations .
  • The paper emphasizes the importance of refining operator definitions to ensure completeness and correctness in problem-space characterization, particularly in the context of water measurement problems involving containers with arbitrary capacities .
  • It discusses the feasibility of automating knowledge creation for cognitive-systems applications, aiming to reduce the need for human mediation in agent knowledge and potentially offering new directions for cognitive-systems research .

What work can be continued in depth?

To delve deeper into the research outlined in the document, several areas can be further explored:

  • Generation of novel problems: Investigating the ability of the CTA Agent to formulate problem spaces for new and unfamiliar problems, ensuring it can generate innovative analyses while avoiding the tendency to replicate known problems .
  • Distinct personas for LLM agents: Developing separate personas within the CTA Agent, such as a quality assurance (QA) engineer role, to enhance performance and address specific needs, potentially leading to more robust systems .
  • Analytic strategies across problem classes: Assessing whether a single analytic strategy is adequate for different problem classes or if distinct strategies are required, particularly in defining problem spaces for various types of problems .
  • Means of information transfer: Exploring different methods for expressing problem-space formulations, ranging from direct code generation to formal specification languages like PDDL, to optimize the utilization of architectural capabilities .

Introduction
Background
Emergence of large language models in AI research
Challenges in problem formulation for AI systems
Objective
To explore LLMs for problem representation and CTA
Reduce human intervention in problem formulation
Enable AI autonomy with domain-general methods
Method
Data Collection
Literature review on problem-solving principles (Polya, Newell)
Analysis of cognitive task analysis frameworks (e.g., GOMS)
Data Preprocessing
Selection and preprocessing of relevant LLM data
Integration of problem-solving strategies into LLM models
LLM-Enabled Cognitive Task Analyst Agent
Model Architecture
Design of the agent for semi-formal specification generation
Comparison of LLM-based and human-in-the-loop systems
Problem Space Definition
Adherence to problem-solving principles
Application of Newell's problem space concept
Strategy Generation
Weak method identification and formulation
Automation of Polya's problem-solving steps
Performance Evaluation
Assessing accuracy and efficiency of generated specifications
Comparison with human-generated problem statements
Limitations and Future Directions
refining problem representations for better accuracy
Multi-modal inputs and LLM improvements
Addressing current model limitations for advanced problem-solving
Conclusion
LLMs' potential in streamlining AI development
Implications for AI autonomy and problem-solving scalability
Recommendations for future research in cognitive systems and LLMs.
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What frameworks, like GOMS, are used to structure problem-solving processes in the study?
How do LLMs contribute to automating cognitive task analysis in the paper's research?
What are the key principles the CTA agent follows when generating problem specifications?
What is the primary focus of the paper regarding large language models and problem-solving?

Eliciting Problem Specifications via Large Language Models

Robert E. Wray, James R. Kirk, John E. Laird·May 20, 2024

Summary

This paper explores the use of large language models (LLMs) in automating problem-solving by translating natural language descriptions into semi-formal specifications for cognitive systems. The authors design an LLM-enabled cognitive task analyst agent that generates problem-space definitions and strategies from AI literature. The goal is to reduce human intervention in problem formulation and enable AI systems to tackle diverse problems using domain-general methods. Key points include: 1. LLMs as a tool for automating problem representation, potentially speeding up AI research. 2. Problem spaces and cognitive task analysis (CTA) frameworks, like GOMS, are used to structure problem-solving processes. 3. The CTA agent generates problem specifications adhering to Polya's problem-solving principles and Newell's definition of problem spaces. 4. The agent aims to create problem formulations for weak methods, allowing for autonomous problem-solving. 5. Two alternative system designs are discussed, one relying heavily on LLMs and another with more human intervention. The study highlights the potential of LLMs in streamlining AI development and suggests future directions for multi-modal inputs and more sophisticated problem-solving capabilities. However, it also acknowledges the need for refining problem representations and addressing limitations in current models.
Mind map
Comparison with human-generated problem statements
Assessing accuracy and efficiency of generated specifications
Automation of Polya's problem-solving steps
Weak method identification and formulation
Application of Newell's problem space concept
Adherence to problem-solving principles
Comparison of LLM-based and human-in-the-loop systems
Design of the agent for semi-formal specification generation
Performance Evaluation
Strategy Generation
Problem Space Definition
Model Architecture
Addressing current model limitations for advanced problem-solving
Multi-modal inputs and LLM improvements
refining problem representations for better accuracy
LLM-Enabled Cognitive Task Analyst Agent
Analysis of cognitive task analysis frameworks (e.g., GOMS)
Literature review on problem-solving principles (Polya, Newell)
Enable AI autonomy with domain-general methods
Reduce human intervention in problem formulation
To explore LLMs for problem representation and CTA
Challenges in problem formulation for AI systems
Emergence of large language models in AI research
Recommendations for future research in cognitive systems and LLMs.
Implications for AI autonomy and problem-solving scalability
LLMs' potential in streamlining AI development
Limitations and Future Directions
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Method
Introduction
Outline
Introduction
Background
Emergence of large language models in AI research
Challenges in problem formulation for AI systems
Objective
To explore LLMs for problem representation and CTA
Reduce human intervention in problem formulation
Enable AI autonomy with domain-general methods
Method
Data Collection
Literature review on problem-solving principles (Polya, Newell)
Analysis of cognitive task analysis frameworks (e.g., GOMS)
Data Preprocessing
Selection and preprocessing of relevant LLM data
Integration of problem-solving strategies into LLM models
LLM-Enabled Cognitive Task Analyst Agent
Model Architecture
Design of the agent for semi-formal specification generation
Comparison of LLM-based and human-in-the-loop systems
Problem Space Definition
Adherence to problem-solving principles
Application of Newell's problem space concept
Strategy Generation
Weak method identification and formulation
Automation of Polya's problem-solving steps
Performance Evaluation
Assessing accuracy and efficiency of generated specifications
Comparison with human-generated problem statements
Limitations and Future Directions
refining problem representations for better accuracy
Multi-modal inputs and LLM improvements
Addressing current model limitations for advanced problem-solving
Conclusion
LLMs' potential in streamlining AI development
Implications for AI autonomy and problem-solving scalability
Recommendations for future research in cognitive systems and LLMs.
Key findings
5

Paper digest

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

The paper aims to address the challenge of automatically producing formal specifications of problem spaces and problems using Large Language Models (LLMs) when presented with natural language problem descriptions . This problem is not entirely new, as the paper builds on previous work in knowledge-based systems, engineering psychology, and machine learning that have systematized and codified translation processes from problems to their representations in AI systems . The focus is on leveraging LLMs to automate the translation process and enable the immediate application of weak methods for problem-solving tasks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that a significant portion of knowledge creation for certain cognitive-systems applications can be automated through an automated problem specification approach, which has the potential to reduce the need for human mediation in agent knowledge, leading to faster and less mediated development of future cognitive systems .


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

The paper "Eliciting Problem Specifications via Large Language Models" proposes several new ideas, methods, and models in the field of cognitive-systems applications and problem-solving . Here are some key points from the paper:

  1. Generation for novel problems: The paper discusses the assessment of the feasibility of a Cognitive Task Analysis (CTA) Agent to formulate problem spaces for new problems not in its training set. It explores the ability of the Large Language Models (LLMs) to generate unique problem analyses while avoiding the tendency to reproduce similar problems and solutions from its training data .

  2. Distinct personas for LLM agents: The paper suggests developing distinct personas for LLM agents to enhance performance. It mentions examples like STORM, a system with separate editor and expert agents, to improve text generation comparable to Wikipedia pages. Implementing different roles like quality assurance (QA) engineer within the CTA Agent is also considered .

  3. Integrated vs. distinct analytic strategies: The paper questions whether a single analytic strategy is sufficient for different classes of problems or if distinct strategies are needed. It explores the overlap in defining problem spaces for various types of problems and the effectiveness of a unified analytic approach .

  4. Automated problem specification: The paper highlights the potential of automating problem specification processes using LLMs, reducing the need for human mediation in agent knowledge creation. This automation could lead to faster development of cognitive systems and open up new research directions in cognitive-systems research .

These ideas and approaches outlined in the paper aim to advance the field of cognitive-systems applications by leveraging Large Language Models for problem specification and analysis, offering new perspectives on problem-solving methodologies and automation possibilities . The paper "Eliciting Problem Specifications via Large Language Models" introduces novel approaches and models for problem specification, offering distinct characteristics and advantages compared to previous methods . Here are the key characteristics and advantages highlighted in the paper:

  1. Automated Problem Specification: The paper emphasizes the potential of automating problem specification processes using Large Language Models (LLMs). This automation reduces the need for human intervention in agent knowledge creation, leading to faster development of cognitive systems and offering new research directions in cognitive-systems applications .

  2. Distinct Personas for LLM Agents: The paper suggests developing distinct personas for LLM agents to enhance performance. By implementing separate roles like quality assurance (QA) engineer within the Cognitive Task Analysis (CTA) Agent, the system can improve text generation and problem analysis, similar to the STORM system that uses separate editor and expert agents .

  3. Integrated vs. Distinct Analytic Strategies: The paper explores the question of whether a single analytic strategy is sufficient for different classes of problems or if distinct strategies are needed. It investigates the overlap in defining problem spaces for various types of problems and the effectiveness of a unified analytic approach, aiming to optimize problem-solving methodologies .

  4. Generation for Novel Problems: The paper assesses the feasibility of LLMs to formulate problem spaces for new and unfamiliar problems not in their training set. It focuses on generating unique problem analyses while avoiding the tendency to reproduce similar problems and solutions from the training data, showcasing the capability of LLMs in handling novel problem scenarios .

  5. Efficient Knowledge Creation: The automated problem specification approach presented in the paper has the potential to streamline knowledge creation for cognitive-systems applications, reducing the labor-intensive nature of system development and enhancing the robustness and capabilities of cognitive systems. This approach also addresses skepticism from researchers outside the community regarding the actual capabilities of cognitive systems .

Overall, the characteristics and advantages of the proposed methods in the paper offer innovative solutions for problem specification, automation of cognitive-systems development, and improved performance through distinct personas and analytic strategies .


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 works exist in the field of problem specifications using large language models. Noteworthy researchers in this area include R.E. Wray, J. Kirk, and J.E. Laird, who have contributed to the study of problem-solving using cognitive systems and large language models . The key to the solution mentioned in the paper involves a search from the initial state to the goal state using specified operators, aiming to identify unproductive paths and undesirable states to efficiently solve the problem .


How were the experiments in the paper designed?

The experiments in the paper were designed to explore the feasibility of using agentic workflows with large language models to create problem space specifications for knowledge-lean search in a problem-solving architecture like Soar . The experiments involved running variations with the CTA Agent using GPT3.5 and GPT4, presenting the models with problem instances directly, and comparing the results . The experiments aimed to provide precise and correct problem-space specifications to enable successful search in Soar, with a focus on refining analysis and identifying search control knowledge .


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

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context . Additionally, there is no information provided regarding the open-source status of the code used in the research. For specific details on the dataset and code used for quantitative evaluation, further information or clarification from the source document may be required.


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 substantial support for the scientific hypotheses that needed verification. The feasibility evaluation conducted using the CTA Agent with GPT3.5 and GPT4 models, along with a one-shot problem-space formulation, demonstrated the effectiveness of the models in problem-solving tasks . The comparison between GPT3.5 and GPT4 showed that GPT4 excelled in generating formal descriptions of the problem space, indicating an improvement in performance . Additionally, the detailed analysis of the sensitivity of the models and the comparison of their outcomes highlighted the reliability and precision of the solutions generated by the models .

Moreover, the experiments included test cases such as F(4, 9) → 6, which aimed to deliver a specific amount of water using containers without graduated markings, showcasing the models' ability to solve complex problems efficiently . The results of the experiments, including the search states explored and the failure detection rates, indicated the models' reliability in finding solutions even for challenging problems . The experiments also emphasized the importance of search control knowledge in optimizing the problem-solving process .

In summary, the experiments and results presented in the paper offer strong empirical evidence supporting the effectiveness and reliability of the large language models in problem specification tasks. The detailed analysis of the models' performance, sensitivity, and search control knowledge provides valuable insights into their capabilities and highlights their potential for various applications in problem-solving domains .


What are the contributions of this paper?

The paper makes several contributions:

  • It provides a detailed analysis of the initial set of operators, highlighting the need for minor adjustments and clarifications, especially in defining post-conditions for transfer operations .
  • The paper emphasizes the importance of refining operator definitions to ensure completeness and correctness in problem-space characterization, particularly in the context of water measurement problems involving containers with arbitrary capacities .
  • It discusses the feasibility of automating knowledge creation for cognitive-systems applications, aiming to reduce the need for human mediation in agent knowledge and potentially offering new directions for cognitive-systems research .

What work can be continued in depth?

To delve deeper into the research outlined in the document, several areas can be further explored:

  • Generation of novel problems: Investigating the ability of the CTA Agent to formulate problem spaces for new and unfamiliar problems, ensuring it can generate innovative analyses while avoiding the tendency to replicate known problems .
  • Distinct personas for LLM agents: Developing separate personas within the CTA Agent, such as a quality assurance (QA) engineer role, to enhance performance and address specific needs, potentially leading to more robust systems .
  • Analytic strategies across problem classes: Assessing whether a single analytic strategy is adequate for different problem classes or if distinct strategies are required, particularly in defining problem spaces for various types of problems .
  • Means of information transfer: Exploring different methods for expressing problem-space formulations, ranging from direct code generation to formal specification languages like PDDL, to optimize the utilization of architectural capabilities .
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