Evaluating Large Language Model Outputs: Comparing Criteria Developed by Domain Experts, Lay Users, and the Models Themselves

核心概念

Domain experts, lay users, and Large Language Models (LLMs) develop distinct sets of evaluation criteria for assessing LLM outputs, with domain experts providing the most detailed and specific criteria, lay users emphasizing formatting and clarity, and LLMs generating more generalized criteria based on prompt keywords.

摘要

The study explores how domain experts, lay users, and LLMs develop evaluation criteria for assessing the outputs of Large Language Models (LLMs). The key findings are:

1. Domain experts set more detailed and specific evaluation criteria compared to lay users and LLMs, reflecting their deep domain knowledge. They focused on accuracy, safety, and providing personalized guidance.

2. Lay users placed greater emphasis on the formatting and clarity of the output, prioritizing readability and ease of understanding over technical details.

3. The LLM generated more generalized criteria, often directly based on keywords from the prompt, and tended to focus on following prompt instructions rather than providing in-depth domain-specific assessments.

4. In the a posteriori stage, after reviewing the LLM outputs, all groups introduced new criteria, with domain experts particularly emphasizing formatting and decision support themes.

The study highlights the complementary strengths of domain experts, lay users, and LLMs in the evaluation process and suggests implications for designing workflows that leverage these strengths at different stages of the evaluation.

统计

Domain experts provided more detailed and specific criteria compared to lay users and LLMs.
Lay users focused more on formatting and clarity of the output, while LLMs generated more generalized criteria based on prompt keywords.
All groups introduced new criteria in the a posteriori stage, with domain experts emphasizing formatting and decision support themes.

引用

"Give examples of high-fiber and low added sugar breakfast options that include at least 8 grams of protein per serving." (NutExp2)
"The response avoids promoting overly restrictive or fad diets." (LLM)
"Should provide the quadratic formula as a clear fraction with a numerator and denominator." (PedExp2)

从中提取的关键见解

Comparing Criteria Development Across Domain Experts, Lay Users, and Models in Large Language Model Evaluation

by Annalisa Szy... 在 arxiv.org 10-04-2024

https://arxiv.org/pdf/2410.02054.pdf

更深入的查询

How can the complementary strengths of domain experts, lay users, and LLMs be effectively integrated into a comprehensive evaluation workflow for LLM outputs?

To effectively integrate the complementary strengths of domain experts, lay users, and LLMs into a comprehensive evaluation workflow for LLM outputs, a staged approach can be adopted. This workflow should leverage the unique contributions of each group at different stages of the evaluation process.

Initial Criteria Development (A Priori Stage): Domain experts should be involved at the outset to establish detailed and contextually relevant evaluation criteria. Their specialized knowledge ensures that the criteria are precise and aligned with domain-specific standards. For instance, in fields like healthcare or education, experts can set criteria that address ethical considerations and specific knowledge requirements.

User-Centric Refinement (A Posteriori Stage): After the initial criteria are set, lay users can provide valuable insights based on their lived experiences and usability concerns. They can refine the criteria by focusing on clarity, accessibility, and practical application. For example, lay users might suggest formatting changes or emphasize the need for clear explanations, which can enhance the overall user experience.

LLM Support for Iteration: LLMs can assist in generating and refining criteria based on the outputs they produce. By analyzing the initial criteria and the generated outputs, LLMs can identify gaps or areas for improvement, suggesting modifications that align with both expert and user expectations. This iterative process allows for continuous enhancement of the evaluation criteria.

Feedback Loops: Establishing feedback loops where domain experts, lay users, and LLMs can review and refine each other's criteria can lead to a more robust evaluation framework. For instance, after lay users provide feedback on the clarity of outputs, domain experts can reassess the criteria to ensure they still meet the necessary standards while incorporating user suggestions.

Documentation and Analysis: Throughout the evaluation process, documenting the criteria and the rationale behind changes is crucial. This transparency allows for better understanding and future reference, ensuring that the evaluation workflow evolves based on empirical evidence and user feedback.

By integrating these strengths, the evaluation workflow can become more comprehensive, ensuring that LLM outputs are not only accurate and reliable but also user-friendly and contextually appropriate.

What are the potential challenges and limitations in relying solely on LLM-generated criteria for evaluating domain-specific outputs?

Relying solely on LLM-generated criteria for evaluating domain-specific outputs presents several challenges and limitations:

Lack of Domain-Specific Knowledge: LLMs, while capable of generating criteria based on prompts, often lack the deep, specialized knowledge that domain experts possess. This can lead to generalized criteria that may not adequately address the nuances and complexities of specific fields, such as healthcare or education. For example, an LLM might suggest broad dietary guidelines without understanding the specific nutritional needs of individuals with certain health conditions.

Surface-Level Understanding: LLMs tend to generate criteria based on keywords and patterns observed in training data, which can result in superficial evaluations. This approach may overlook critical details that domain experts would consider, such as ethical implications or the latest research findings relevant to the domain.

Inability to Contextualize: LLMs may struggle to contextualize criteria within the specific scenarios they are evaluating. For instance, in a pedagogical context, an LLM might not fully grasp the importance of tailoring explanations to different learning styles, which is something that experienced educators would prioritize.

Criteria Drift: The phenomenon of "criteria drift," where evaluation criteria evolve during the assessment process, can be exacerbated when relying solely on LLMs. Without human oversight, the criteria generated may not adapt appropriately to the specific needs of the evaluation, leading to inconsistencies and potential inaccuracies in the assessment of outputs.

Ethical and Safety Concerns: In sensitive domains like healthcare, relying solely on LLM-generated criteria can pose ethical risks. LLMs may inadvertently generate criteria that promote unsafe practices or fail to consider the ethical implications of certain recommendations, which domain experts are trained to navigate.

Limited User Perspective: LLMs do not possess the lived experiences and practical insights that lay users bring to the evaluation process. This can result in criteria that do not prioritize usability or accessibility, ultimately affecting the effectiveness of the outputs for end-users.

In summary, while LLMs can play a valuable role in generating evaluation criteria, their limitations necessitate the involvement of domain experts and lay users to ensure that the evaluation process is comprehensive, accurate, and contextually relevant.

How might the criteria-setting process evolve if domain experts, lay users, and LLMs were able to iteratively refine each other's criteria over multiple rounds of evaluation?

If domain experts, lay users, and LLMs were able to iteratively refine each other's criteria over multiple rounds of evaluation, the criteria-setting process could evolve significantly in several ways:

Enhanced Collaboration: The iterative process would foster a collaborative environment where domain experts, lay users, and LLMs can engage in meaningful dialogue. This collaboration would allow for the sharing of insights and perspectives, leading to a more holistic understanding of the evaluation criteria. For instance, domain experts could explain the rationale behind specific criteria, while lay users could provide feedback on how these criteria translate into practical applications.

Dynamic Criteria Development: The criteria would become more dynamic and adaptable, evolving in response to real-time feedback from all parties involved. As domain experts refine criteria based on user feedback, and lay users adjust their expectations based on expert insights, the criteria would better reflect the complexities of the domain and the needs of the end-users.

Increased Specificity and Relevance: With continuous input from domain experts, lay users, and LLMs, the criteria would likely become more specific and relevant to the context. Domain experts would ensure that the criteria are grounded in current knowledge and best practices, while lay users would help ensure that the criteria are understandable and applicable in real-world scenarios.

Improved Accuracy and Reliability: The iterative refinement process would enhance the accuracy and reliability of the evaluation criteria. By allowing domain experts to assess the outputs generated based on the criteria and providing feedback on their effectiveness, the criteria can be fine-tuned to ensure they meet the necessary standards for quality and relevance.

Feedback-Driven Adjustments: The iterative nature of the process would enable quick adjustments based on feedback. For example, if lay users identify that certain criteria are not yielding useful outputs, they can communicate this to domain experts, who can then modify the criteria accordingly. This responsiveness would lead to a more effective evaluation process.

Cross-Pollination of Ideas: The interaction between domain experts, lay users, and LLMs would facilitate the cross-pollination of ideas, leading to innovative approaches to criteria-setting. For instance, lay users might suggest new criteria based on their experiences, which domain experts could then validate and incorporate into the evaluation framework.

Comprehensive Evaluation Framework: Ultimately, the iterative refinement process would contribute to the development of a comprehensive evaluation framework that integrates the strengths of all three groups. This framework would be better equipped to address the complexities of domain-specific outputs, ensuring that evaluations are thorough, contextually relevant, and user-friendly.

In conclusion, enabling domain experts, lay users, and LLMs to iteratively refine each other's criteria would lead to a more robust, dynamic, and effective criteria-setting process, ultimately enhancing the quality of LLM outputs across various domains.