A New Way to Evaluate Large Language Models

Evaluating large language models (LLMs) is important to understand how well they perform on different tasks. Traditional methods usually apply the same approach to all tasks, which may not accurately reflect the complexity of each task. To solve this problem, we introduce a new way to evaluate LLMs called the Hierarchical Prompting Taxonomy (HPT). This system uses a framework with different types of prompts, ranging from simple to complex, to measure how well LLMs can handle various tasks.

#The Need for Better Evaluation Methods

Large language models have changed the field of natural language processing, providing significant improvements in many applications. However, it remains a challenge to assess how well these models perform across different datasets and tasks. Traditional prompting methods often lead to poor evaluations, as they treat all tasks equally without considering their complexity. This highlights the need for better evaluation strategies that can adapt to different levels of task difficulty.

#Hierarchical Prompt Framework (HPF)

The Hierarchical Prompt Framework (HPF) consists of five different Prompting Strategies, each suited for different levels of task complexity. This ensures that the model receives the right prompt based on the task's requirements. Here are the five strategies:

1. Role Prompting: The model is given a specific role to play without any detailed context.
2. Zero-Shot Chain-of-Thought Prompting: The model is asked to think through a problem step by step without examples.
3. Three-Shot Chain-of-Thought Prompting: The model receives three examples to guide its reasoning.
4. Least-to-Most Prompting: The model is guided through simpler tasks before tackling more complicated ones.
5. Generated Knowledge Prompting: The model incorporates additional information to enhance its understanding of the task.

By following these strategies, the evaluation process becomes more effective and insightful.

#Introducing the Hierarchical Prompting Taxonomy (HPT)

The Hierarchical Prompting Taxonomy (HPT) offers a structured approach to evaluate how well LLMs perform on diverse tasks. Each prompt type is organized based on task complexity, allowing for a clearer understanding of a model's abilities. The HPT generates a score called the Hierarchical Prompting Score (HP-Score), which indicates how well the model can handle different tasks.

#Adaptive Hierarchical Prompt Framework

We also introduce an Adaptive Hierarchical Prompt framework, which automates the selection of the most appropriate prompting strategy for each task. This method uses a prompt-selector to determine the best approach based on the task's complexity, making the evaluation process more efficient.

#Experiments and Findings

To demonstrate the effectiveness of HPT, we compared the manual and adaptive HP frameworks using four instruction-tuned LLMs: Llama 3 8B, Phi 3 3.8B, Mistral 7B, and Gemma 7B. We conducted experiments on four datasets: BoolQ, CommonSenseQA (CSQA), IWSLT-2017 en-fr, and SamSum. The results show that HPT provides a reliable way to evaluate LLMs and understand their capabilities better.

#Dataset Descriptions

1. BoolQ: A dataset with approximately 16,000 True/False questions based on passages from Wikipedia.
2. CommonSenseQA (CSQA): Contains around 12,000 multiple-choice questions to evaluate the models' commonsense reasoning.
3. IWSLT-2017 en-fr: A parallel dataset with English-French sentence pairs used for machine translation.
4. SamSum: Features around 16,000 human-generated chat logs with summaries for dialogue summarization.

#Evaluation Results

In our experiments, we measured the performance of the four LLMs on different datasets, comparing manual HPF and adaptive HPF scores.

1. BoolQ: All LLMs performed well, with Llama 3 8B achieving the best results.
2. CommonSenseQA: Phi 3 3.8B excelled in solving this dataset.
3. IWSLT: All models struggled with this task, highlighting their limitations.
4. SamSum: Performance varied, with some models performing better than others.

The manual HPF consistently outperformed adaptive HPF in most cases, showing that direct approach is more reliable in evaluating models.

#The Importance of Prompting Strategies

Prompting is a central aspect of how LLMs work. The way we design prompts can significantly influence the model's responses. Effective prompting strategies can lead to better performance on tasks ranging from simple questions to complex reasoning. Recent research has explored many approaches to improve model performance, including various prompting and reasoning techniques.

#Types of Prompting Strategies

• Role Prompting: A straightforward technique that defines a role for the model. While simple, it may not produce the most accurate results.
• Chain-of-Thought (CoT) Prompting: Encourages step-by-step reasoning by guiding the model through the problem-solving process.
• Progressive Hint Prompting: Uses hints to guide the model toward producing correct answers.
• Metacognitive Prompting: Incorporates self-evaluation, allowing the model to enhance its understanding.

These strategies, especially when applied based on task complexity, yield better outcomes.

#Manual vs. Adaptive Frameworks

We evaluated both manual and adaptive frameworks to determine which approach works better. The manual HPF provides more consistent results, especially in handling complex tasks. In contrast, the adaptive HPF struggled with hallucinations, which are instances where the model generates incorrect or misleading responses.

1. Manual HPF: Provides reliable outcomes and is better suited for evaluating diverse tasks.
2. Adaptive HPF: Faces challenges in selecting the appropriate prompting levels, leading to higher scores that reflect poor performance.

#Limitations and Future Work

Our research has certain limitations that should be addressed in future studies. These include:

1. Limited Model Evaluation: We focused on four specific LLMs. Exploring a wider variety of models may enhance our findings.
2. Restricted Dataset Evaluation: The datasets used were limited in scope. Including more diverse datasets could provide a broader evaluation.
3. Prompt Design: Crafting high-quality prompts requires expertise. Future work should focus on improving prompt strategies and exploring more innovative techniques.
4. Adaptive Framework Challenges: The Adaptive HPF relies on a prompt-selector, which can lead to hallucinations. Further research is needed to improve its efficiency.

#Ethical Considerations

The HP-Scores given by experts can introduce bias into our analysis. Individual experiences and perspectives may influence their scoring. Nonetheless, using publicly available datasets minimizes ethical risks. Therefore, it’s essential to acknowledge any potential biases to maintain transparency in our evaluation.

#Conclusion

The Hierarchical Prompting Taxonomy (HPT) provides a valuable framework for evaluating large language models. By employing different prompting strategies based on task complexity, we can gain deeper insights into how well these models perform.

The results indicate that task complexity significantly impacts model performance. Manual HPF tends to yield more reliable results compared to the adaptive approach, revealing the need for careful prompting strategies in model evaluation.

Future work should focus on expanding the evaluation framework to include more models and datasets, refining prompt design, and exploring ways to enhance the adaptive framework's efficiency. Overall, HPT offers a promising path for the evaluation of LLMs, paving the way for further advancements in natural language processing.