Evaluating Large Language Model Generations Using a Panel of Diverse Models

Kernekoncepter

Using a Panel of LLM Evaluators (PoLL) composed of diverse models outperforms a single large judge model like GPT-4 in terms of correlation with human judgments, reduced intra-model bias, and lower cost.

Resumé

The paper proposes using a Panel of LLM Evaluators (PoLL) instead of a single large model like GPT-4 to evaluate the quality of generations from large language models (LLMs).

Key highlights:

• Evaluating LLM generations is challenging as it is difficult to find meaningful data and evaluate the correctness of free-form generations.
• Many evaluations now use LLMs themselves as judges to score the quality of outputs from other LLMs, often using a single large model like GPT-4.
• This approach has been shown to introduce intra-model bias and the large models are often unnecessary.
• The authors propose using a PoLL composed of multiple smaller models from different families to evaluate LLM generations.
• Across three distinct evaluation settings and six datasets, the PoLL outperforms a single large judge, exhibits less intra-model bias, and is over seven times less expensive.
• The authors find that in some scenarios, GPT-4 is a relatively weak judge, exhibiting high variance with minor changes to the prompt.
• Intra-model scoring bias is reduced by pooling judgments across a panel of heterogeneous evaluator models.

Statistik

The water from the monsoon determines whether water will reach Lake Eyre and how deep the lake will get.
The average rainfall in the area of Lake Eyre is 150 mm per year.
The altitude usually attributed to Kati Thanda-Lake Eyre refers to the deepest parts of the lake floor, in Belt Bay and the Madigan Gulf.

Citater

"As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality."
"To address this, many evaluations now rely on using LLMs themselves as judges to score the quality of outputs from other LLMs."
"We propose instead to evaluate models using a Panel of LLm evaluators (PoLL)."

Vigtigste indsigter udtrukket fra

Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models

by Pat Verga,Se... kl. arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18796.pdf

Dybere Forespørgsler

How can the panel composition and voting function in PoLL be further optimized to improve evaluation performance?

To further optimize the panel composition and voting function in PoLL for improved evaluation performance, several strategies can be considered:

Diverse Model Selection: Ensuring a diverse set of models from different families can help capture a broader range of perspectives and reduce bias. Including models with varying strengths and weaknesses can provide a more comprehensive evaluation.

Dynamic Panel Composition: Implementing a dynamic panel composition strategy where models are added or removed based on their performance and relevance to the task at hand. This adaptive approach can enhance the overall effectiveness of the panel.

Optimized Voting Mechanism: Experimenting with different voting mechanisms such as weighted voting based on the performance of individual models, or incorporating a confidence score for each model's judgment can lead to more accurate and reliable evaluations.

Regular Calibration: Regularly calibrating the panel by comparing its judgments with human annotations and adjusting the composition or voting strategy accordingly can help maintain the panel's effectiveness over time.

What are the potential drawbacks or limitations of using a PoLL approach compared to a single large judge model?

While the PoLL approach offers several advantages, it also comes with some potential drawbacks and limitations:

Complexity: Managing a panel of diverse models can be more complex and resource-intensive compared to a single large judge model. Coordinating multiple models, ensuring consistency, and handling disagreements among judges can add complexity to the evaluation process.

Cost: Running multiple models in parallel can incur higher costs compared to a single large judge model, especially if each model requires individual resources and infrastructure.

Agreement Challenges: Ensuring agreement among a diverse panel of models may be challenging, as different models may have varying interpretations and biases. Resolving disagreements and achieving consensus can be time-consuming.

Scalability: Scaling up a PoLL approach to evaluate a large volume of data or tasks may pose scalability challenges, especially if the panel composition needs to be adjusted frequently.

How can the insights from this work on LLM evaluation be applied to improve the evaluation of other types of AI systems beyond just language models?

The insights from this work on LLM evaluation can be applied to enhance the evaluation of other types of AI systems in the following ways:

Model Diversity: Similar to the PoLL approach, incorporating a diverse set of models from different families can provide a more comprehensive evaluation of AI systems across various tasks and domains.

Bias Reduction: Implementing panel-based evaluation methods can help reduce bias in the assessment of AI systems by leveraging multiple perspectives and mitigating the impact of individual model biases.

Dynamic Evaluation: Adopting dynamic evaluation strategies that adapt the evaluation criteria based on the task requirements and model performance can lead to more accurate and relevant assessments of AI systems.

Cost-Efficiency: Exploring cost-effective evaluation approaches, such as pooling judgments from multiple models or optimizing voting mechanisms, can help streamline the evaluation process and make it more cost-efficient.

By leveraging the principles of diversity, bias reduction, dynamic evaluation, and cost-efficiency derived from LLM evaluation, the evaluation of other AI systems can be enhanced to ensure robust and reliable assessments across different applications and use cases.