Paper: Hypothesis- and Structure-based prompting for medical and business diagnosis (ICLR-2024)

Reviewer comment (Experiments):
The proposed HS prompting techniques seem to be an extension of existing methods such as ToT and GoT. Yet, the authors did not include these methods as their baselines, making it hard to evaluate the effectiveness of the HS prompting.

Author response:
In response to your observation about the HS method and its relation to ToT and GoT, we clarify that while there are conceptual similarities, the application context of HS is distinct. The interactive, user-driven nature of our tasks, where the model's questions and the user's responses continuously shape the problem-solving process, differs fundamentally from the more structured approach of ToT and GoT. This difference in task structure and interaction pattern influenced our decision to focus on HS without including these methods as direct baselines, as their integration presented practical challenges given the nature of our tasks

Reviewer comment (Evaluation):
In the experiments, the criteria used to evaluate the models were not clearly defined and justified. For example, the background of the professionals and their scoring standards is not clear. It is also unclear whether the experts who set the criteria participated in the model scoring process (which could lead to potential bias).

Author response:
We appreciate your attention to the evaluation criteria and the background of the professionals involved. As detailed in Sections 4.1 and 4.2 of our paper, the business consultants evaluating our models hold MBAs or have over five years of experience from reputable consulting firms. In the medical domain, our evaluators are licensed doctors with more than five years of clinical experience, specializing in various fields including cardiology and orthopedics. The scoring standards followed by these professionals are based on the criteria outlined in Section 4. We acknowledge the importance of an unbiased evaluation process and confirm that the experts who set the criteria were not involved in the scoring process. This will be clarified in the revised version of our paper.

Reviewer comment (Evaluation):
The business case study did not show a significant advantage for the proposed prompting techniques. More discussion on the cause is needed.

Author response:
For insights into the performance of our prompting techniques in business cases, we refer you to Figure 2 of our paper. This figure demonstrates that the HS method consistently scores higher than other baseline methods in business scenarios when averaged across different cases.

Reviewer comment (Evaluation):
More objective metrics should be considered in the experiments. For example, runtime, user feedback statistics, and other quantitative metrics.

Author response:
Thank you for raising the issue of incorporating more objective metrics into our evaluation process. We understand the importance of quantifiable measures in research. However, for the specific nature of our tasks, which focus on generating insightful questions and hypotheses to identify root causes in many-to-one problems, human expert evaluations, such as user feedback statistics and interviews, are the most appropriate and informative methods of assessment.

The complexity of our tasks lies in evaluating the effectiveness of GPT-4's problem-solving approach, not merely in determining whether it arrives at the correct answer. This qualitative aspect of problem-solving, which involves understanding and evaluating the reasoning process, makes it challenging to apply traditional objective metrics. We kindly ask for your understanding regarding the difficulty in incorporating other types of objective metrics for this particular study.

Reviewer comment (Presentation):
Presentation issues: Some data charts seem misaligned with the text. The paper requires a major revison.

Author response:
We are grateful for your feedback regarding the alignment of data charts with the text. We acknowledge the importance of a clear and accurate presentation and will undertake a major revision to ensure that all charts and text are correctly aligned and presented coherently.

Reviewer comment (Experiments):
You assess your method on only 3 business cases for business consulting. This is dramatically too few examples on which to base any generalizable conclusion for the performance of this method. You need to assess this method on a much larger set of business consulting problems in order to argue that your approach has merit over alternative approaches in a reliable manner that should be expected to generalize across a meaningful subset of business consulting problems.

Author response:
Thank you, reviewer hnXU, for your continued engagement and feedback. We understand your concern regarding the limited number of case studies in our research. We wish to emphasize that conducting validations in complex domains such as business consulting and medical diagnosis is both financially and temporally demanding. As outlined in our ethics statement section, the compensation for participants assessing these case studies was significant, resulting in a total expenditure of £600 for the survey. This budget constraint played a key role in limiting the number of cases we could feasibly include. Despite these limitations, we carefully selected the most representative cases for each domain to ensure the quality and relevance of our findings. We hope that our preliminary results will serve as a foundation for future research, allowing others to build upon and expand our work

Reviewer comment (Experiments):
Similarly for medical cases, you need to experiment with more than 4 cases (I know you started with 5, but one ended up being excluded).

Author response:
Thank you, reviewer hnXU, for your continued engagement and feedback. We understand your concern regarding the limited number of case studies in our research. We wish to emphasize that conducting validations in complex domains such as business consulting and medical diagnosis is both financially and temporally demanding. As outlined in our ethics statement section, the compensation for participants assessing these case studies was significant, resulting in a total expenditure of £600 for the survey. This budget constraint played a key role in limiting the number of cases we could feasibly include. Despite these limitations, we carefully selected the most representative cases for each domain to ensure the quality and relevance of our findings. We hope that our preliminary results will serve as a foundation for future research, allowing others to build upon and expand our work

Reviewer comment (Experiments):
The quoted justification below for rejecting baselines feels insufficient. Your framework is also repetitive (in that it is recursive) to arrive at a single end point, and the fact that you use ChatGPT rather than the API does not invalidate studies that require API use, as you could (and should, for robustness in your experiments) be able to implement your study using a simple program that leverages the API. Quoted justification: "While these methods utilize the GPT-4 API to integrate tree or graph search algorithms, they’re not directly adaptable to our chat interface, where humans interact with the model. Furthermore, these methods best suit tasks that can be broken down into repetitive steps, with a clearly defined endpoint, while business and medical diagnosis tasks are not."

Author response:
We appreciate your critique of our justification for not using certain baseline methods. While our framework does involve a recursive approach, the nature of our tasks, which involve dynamic and user-responsive problem-solving, does not align well with the predetermined search patterns of methods that require API usage. The key difference lies in the unpredictability and flexibility of the problem-solving steps in our tasks, which contrasts with the more defined and repetitive structures suited to API-driven methods. Our decision to prioritize the HS method was based on its compatibility with the evolving and interactive nature of our study's tasks.

Reviewer comment (Writing):
IO is not clearly defined in your work. CoT is defined as an acronym, but you don't explicitly indicate what exact prompting strategy is used and how it differs from your approach to justify your experiments.

Author response:
Regarding the definitions of IO (input-output method) and CoT (Chain-of-Thought method), we adhere to the definitions provided in Wei et al., 2022, as cited in our paper. We believe these established definitions accurately represent the methodologies we reference and compare against in our work.

Wei, Jason, et al. "Chain-of-thought prompting elicits reasoning in large language models." Advances in Neural Information Processing Systems 35 (2022): 24824-24837.

Reviewer comment (Evaluation):
A key part of the challenge here is one of evaluation; which evaluation metrics should be used, how can they be efficiently assessed at scale, what factors of the input motivate success or failure on different metrics, etc. You do not offer any significant commentary on these challenges nor offer solutions for them, which significantly undercuts your impact here.

Author response:
Addressing the challenge of evaluation, we recognize that in business consulting and medical diagnosis, standard evaluation metrics are not always clearly defined. Following the approach of Singhal et al., 2023, we employed human experts to develop criteria tailored to our objectives. Our methodology involved starting with widely recognized criteria and then refining these with domain experts to focus on evaluating GPT-4's ability to generate viable hypotheses and ask relevant questions. This two-step process ensures that our criteria are both relevant and manageable for evaluators.

Reviewer comment (Experiments):
The method heavily relies on GPT-4's ability, which may limit the applicability of the approach to other LLMs. CoT, ToT, and GoT are general methods that can be used in any other LLMs regardless of how much knowledge is stored in LLMs (they act as an aid to LLM reasoners). However, the assumption of HS is that LLMs are very knowledgeable about defining any problem landscape and are great at generating possible hypotheses, which is not just the role of a reasoner but of an oracle-knowledge base and reasoner at the same time. To show the broad applicability of the approach, the authors should use other LLMs to demonstrate the effectiveness of HS (possibly using a retriever if some LLMs do not hold enough knowledge).

Author response:
We appreciate your concern regarding HS's reliance on GPT-4's capabilities. While HS does assume that LLMs possess a degree of logical reasoning, it does not necessitate extensive domain-specific knowledge. The HS framework is designed to guide LLMs in enhancing their domain understanding by prompting them to ask relevant questions, even with limited initial knowledge. For instance, in business scenarios like declining sales, the model, with basic knowledge (e.g., declining sales is due to either new competitors or decreasing product quality), can formulate hypotheses and ask pertinent questions across various domains such as automotive, food, or oil industries. Similarly, in medical contexts, it might start by determining the urgency or nature of a case. This methodology can be adapted to LLMs with varying knowledge capacities. We acknowledge the value of testing HS with less powerful LLMs than GPT-4 and aim to pursue this in future research.

Reviewer comment (Novelty):
The proposed method resembles a human-AI interactive version of ToT or GoT. The idea itself is very practical and useful, but the credibility of this approach depends on the user.

Author response:
Your question about the user's expertise is vital. We want to clarify that HS targets junior experts in business and healthcare, assisting them in identifying root causes with logical, explainable flows. However, the framework is also designed to be accessible to non-experts. In our experiments, we limited the user's role to providing data as requested by GPT-4. The prompts, once generated for a specific domain, can be reused by non-expert users for multiple questions within that domain. This approach, coupled with clear instructions, makes HS more conducive to generating quality examples, surpassing other prompting methods in terms of user guidance.

Reviewer comment (Writing):
How is the citation (Zheng et al., 2023) for neural architecture search related to real-world modeling? Also, I think 'general tasks' should be 'general NLP tasks'.

Author response:
Regarding the citation of Zheng et al., 2023, in the context of neural architecture search, it exemplifies how LLMs utilize domain-specific knowledge (in this case, deep learning model design) to propose optimized neural architectures. LLMs draw on their understanding of the relationship between training loss curves and model complexity to suggest adjustments in model size. This process is indicative of the applicability of LLMs in real-world modelling scenarios. Also, we agree with your suggestion to specify 'general NLP tasks' for clarity and precision.

Zheng, Mingkai, et al. "Can GPT-4 Perform Neural Architecture Search?." arXiv preprint arXiv:2304.10970 (2023).

Reviewer comment (Theory):
How can we guarantee that the options that LLMs offer are MECE if used by a non-expert?

Author response:
Your question about the user's expertise is vital. We want to clarify that HS targets junior experts in business and healthcare, assisting them in identifying root causes with logical, explainable flows. However, the framework is also designed to be accessible to non-experts. In our experiments, we limited the user's role to providing data as requested by GPT-4. The prompts, once generated for a specific domain, can be reused by non-expert users for multiple questions within that domain. This approach, coupled with clear instructions, makes HS more conducive to generating quality examples, surpassing other prompting methods in terms of user guidance.

Reviewer comment (Theory):
Are there citations or backup arguments for the claims below?  
1. 'LLMs need to apply this knowledge in a structured and efficient manner, especially when solving many-to-one problems'  
2. 'While CoT excels in one-to-one mapping problems, it falters when multiple potential root causes must be explored'

Author response:
Thank you for pointing out the missing citations. 

'LLMs need to apply this knowledge in a structured and efficient manner, especially when solving many-to-one problems': Many papers and textbooks emphasise the role of hypothesis-based and structure-based thinking in business.
Eisenmann, Thomas R., Eric Ries, and Sarah Dillard. "Hypothesis-driven entrepreneurship: The lean startup." Harvard business school entrepreneurial management case 812-095 (2012).

'While CoT excels in one-to-one mapping problems, it falters when multiple potential root causes must be explored': We empirically observe the falters of CoT in many-to-one problems, which is the motivation for this research. You can find it in our results. 

We thank the reviewer once again for their thorough assessment and are more than happy to engage further.

Reviewer comment (Experiments):
Limited comparison to existing methods: While the paper enlists domain experts to validate the HS method and provide a comparison to existing baseline methods, the comparison is limited to a few specific methods. It would be beneficial to see a more comprehensive comparison to a wider range of existing methods.

Author response:
Thank you for suggesting a broader comparison with existing methods. As we answer in the common response, we acknowledge the importance of such comparative analysis. However, our decision to not include methods like ToT and GoT as baselines were influenced by the challenges in integrating their structured search algorithms into our unique task design. Our HS method focuses on an interactive model where the user's input dynamically shapes the conversation, which differs significantly from the more static and predefined pathways of these baseline methods. Thus, while we understand the value of a comprehensive comparison, the distinct nature of our interactive tasks makes such a direct comparison challenging.

Reviewer comment (Experiments):
The paper focuses on the application of the HS method to healthcare and business diagnosis, and it is unclear how generalizable the approach is to other domains or problem types.

Author response:
We acknowledge the potential applicability of the HS method in other fields such as finance and engineering. However, these domains fall outside the scope of our current research. Our primary focus is on addressing many-to-one problems, particularly those involving the identification of a primary cause from multiple symptoms or observations. This focus guided our choice of business consulting and medical diagnostics as the primary application areas for this study.

In future work, we aim to explore the extension of the HS method to additional domains and further evaluate its effectiveness across a broader range of applications. This expansion will enable us to better understand the generalizability and utility of the HS method in various real-world contexts.

Reviewer comment (Experiments):
The paper's qualitative evaluation is limited to a few cases with a panel of consultants or medical doctors. Performance consistency is a major concern with such limited of observed samples.

Author response:
Thank you for the feedback regarding the application of the HS method across various real-world scenarios. Regarding the inclusion of more case studies in healthcare and business, as mentioned in our common response, our selection was limited to three business consulting cases and four medical cases, primarily due to budget constraints for expert evaluations. These cases were chosen carefully to represent diverse scenarios within business consulting and medical diagnostics, specifically focusing on chest pain diagnosis in healthcare.