Evaluating Large Language Models: A Critical Review of Challenges and Recommendations for Reproducible, Reliable, and Robust Assessments

מושגי ליבה

Evaluating large language models (LLMs) is complex and often inconsistent, hindering reproducibility, reliability, and robustness; this review identifies key challenges and provides recommendations for improved evaluation practices.

תקציר

• Bibliographic Information: Laskar, M.T.R., Alqahtani, S., Bari, M.S., Rahman, M., Khan, M.A.M., Khan, H., Jahan, I., Bhuiyan, M.A.H., Tan, C.W., Parvez, M.R., Hoque, E., Joty, S., & Huang, J.X. (2024). A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations. arXiv preprint arXiv:2407.04069v2.

• Research Objective: This paper aims to systematically review the challenges and limitations in evaluating large language models (LLMs) and provide recommendations for more reliable, reproducible, and robust assessments.

• Methodology: The authors conduct a critical review of existing literature on LLM evaluation, focusing on three key dimensions: reproducibility, reliability, and robustness. They analyze common practices in each stage of the evaluation pipeline, identifying inconsistencies and limitations.

• Key Findings: The review reveals significant inconsistencies in benchmark selection, data integrity, prompt engineering, decoding strategies, parsing script design, and evaluation metrics. These inconsistencies hinder the ability to reproduce results, trust the reliability of findings, and assess the generalizability of LLM performance.

• Main Conclusions: The authors argue that standardized and systematic evaluation protocols are crucial for ensuring the reliable use of LLMs in real-world applications. They provide recommendations for each stage of the evaluation pipeline, emphasizing transparency, data integrity, and the use of diverse benchmarks and evaluation metrics.

• Significance: This review provides valuable insights into the current state of LLM evaluation and highlights the need for more rigorous and standardized practices. The recommendations offered can guide researchers and practitioners in conducting more reliable and informative LLM assessments.

• Limitations and Future Research: The paper focuses primarily on text-based NLP tasks and does not extensively cover the evaluation of multimodal LLMs. Future research could explore these areas and develop standardized protocols for evaluating LLMs across different modalities and languages.

סטטיסטיקה

90.6% of 212 analyzed papers on LLM evaluation did not share their prompts.
Only 20.7% of 212 analyzed papers shared their model versions.
Vocabulary coverage of LLM tokenizers decreases as benchmarking datasets become more diverse and complex.
In Open-Domain QA, human-in-the-loop evaluation shows up to a 10% difference compared to purely automatic evaluation.
Parsing script-based automatic evaluation can be unreliable, as demonstrated by discrepancies observed in the SQuAD-V2 dataset.

ציטוטים

"Evaluating LLMs is as complex and resource-intensive as their development, involving multiple levels or aspects."
"The continuous updates of the closed-source models, often with undisclosed changes can also impact reproducibility."
"With the current generation of LLMs being extremely capable of learning new skills with minimal amounts of data, exposing them to evaluation data may undermine the measurement of their true capabilities."
"Minor prompt variations can lead to diverse outcomes for different models […], highlighting the need to compare benchmarks across multiple prompts."

תובנות מפתח מזוקקות מ:

A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations

by Md Tahmid Ra... ב- arxiv.org 10-04-2024

https://arxiv.org/pdf/2407.04069.pdf

שאלות מעמיקות

How can we develop standardized evaluation protocols that are adaptable to the rapidly evolving landscape of LLMs and address the challenges posed by closed-source models?

Developing standardized evaluation protocols for LLMs in a rapidly evolving landscape, especially with the challenges of closed-source models, requires a multi-faceted approach:
1. Focus on Core Capabilities and Generalization:

Standardized Test Suites: Instead of solely relying on specific benchmark datasets, design test suites that evaluate core LLM capabilities like reasoning, common-sense understanding, factual accuracy, and contextual awareness. These test suites should be designed to be domain-agnostic and measure the model's ability to generalize to new tasks and domains.
Open-Source Benchmarking Platforms: Encourage the development and adoption of open-source benchmarking platforms that provide a common ground for evaluating LLMs. These platforms should be designed to be easily extensible and updated to accommodate new tasks, datasets, and evaluation metrics.
2. Addressing Closed-Source Challenges:

Black-Box Evaluation Metrics: Develop evaluation metrics that can assess closed-source models without requiring access to their internal workings. This could involve focusing on input-output behavior analysis, such as measuring the consistency, coherence, and factual grounding of generated text across different prompts and contexts.
Collaboration and Transparency: Foster collaboration between researchers and developers of both open and closed-source LLMs. Encourage the sharing of best practices, evaluation methodologies, and even anonymized model outputs to facilitate more comprehensive and reliable evaluations.
3. Adaptability and Continuous Evolution:

Modular Evaluation Frameworks: Design evaluation protocols that are modular and adaptable. This allows for the incorporation of new evaluation metrics, tasks, and datasets as the field progresses and new challenges emerge.
Community-Driven Development: Encourage community involvement in the development and refinement of evaluation protocols. This ensures that the protocols remain relevant, up-to-date, and reflect the evolving needs of the LLM research and development community.
4. Addressing Reproducibility and Transparency:

Standardized Reporting Guidelines: Establish clear and comprehensive reporting guidelines for LLM evaluations. This includes providing detailed information about the model's training data, architecture, hyperparameters, evaluation setup, and any data preprocessing steps.
Code and Data Sharing: Encourage the sharing of code and data used for evaluation whenever possible. This allows for greater transparency and facilitates the reproducibility of results.
By focusing on these principles, we can create standardized evaluation protocols that are robust, adaptable, and can provide meaningful insights into the capabilities and limitations of both open and closed-source LLMs.

Could focusing on evaluating the robustness and generalization capabilities of LLMs, rather than solely on benchmark performance, provide a more realistic assessment of their real-world applicability?

Yes, absolutely. Focusing on robustness and generalization capabilities is crucial for a realistic assessment of LLMs' real-world applicability. While benchmark performance provides a valuable snapshot of a model's capabilities on specific tasks, it doesn't necessarily translate to success in real-world scenarios, which are often more complex, unpredictable, and require adaptability.
Here's why focusing on robustness and generalization is essential:

Real-World Data is Messy: Unlike curated benchmark datasets, real-world data is often noisy, incomplete, and inconsistent. A robust LLM should be able to handle these imperfections gracefully without significant performance degradation.
Unseen Tasks and Domains: Real-world applications often involve tasks and domains that were not explicitly part of the LLM's training data. A model with strong generalization capabilities can adapt to these new situations and perform effectively.
Bias and Fairness: LLMs can inherit biases present in their training data, leading to unfair or discriminatory outcomes in real-world applications. Evaluating for robustness includes assessing the model's susceptibility to bias and its ability to perform fairly across different demographics and contexts.
Safety and Reliability: For LLMs to be deployed in critical applications like healthcare or finance, they need to be safe and reliable. This means being resistant to adversarial attacks, producing consistent outputs, and knowing when to flag uncertainty or seek human intervention.
How to Evaluate Robustness and Generalization:

Out-of-Distribution Testing: Evaluate LLMs on datasets and tasks that are significantly different from their training data. This helps assess their ability to generalize to new situations.
Adversarial Testing: Deliberately introduce noise, perturbations, or adversarial examples to the input data to see how well the LLM handles these challenges.
Stress Testing: Test the LLM under extreme conditions, such as very long input sequences, unusual prompts, or resource constraints, to assess its limits and breaking points.
Domain Adaptation Techniques: Evaluate how well the LLM can be fine-tuned or adapted to new domains with limited data.
By shifting the focus from pure benchmark performance to a more holistic evaluation of robustness and generalization, we can gain a more realistic understanding of an LLM's strengths and weaknesses, leading to more responsible and impactful real-world applications.

What are the ethical implications of relying heavily on automated metrics and LLM-based evaluators in assessing the performance of LLMs, and how can we ensure human oversight and judgment remain integral to the evaluation process?

While automated metrics and LLM-based evaluators offer efficiency and scalability in assessing LLM performance, their heavy reliance raises significant ethical implications:
1. Amplifying Existing Biases: Automated metrics are often trained on data that reflects existing societal biases. Over-reliance on such metrics can perpetuate and even amplify these biases in the evaluated LLMs, leading to unfair or discriminatory outcomes.
2. Lack of Nuance and Contextual Understanding: Automated metrics often struggle to capture the nuances of human language and may fail to adequately assess aspects like creativity, humor, or cultural sensitivity. This can lead to a skewed evaluation that prioritizes metrics over genuine understanding.
3. Erosion of Human Values and Judgment: Relying solely on automated evaluation risks sidelining human values and judgment in defining what constitutes "good" language generation. This can lead to LLMs optimized for metrics rather than for human-centered communication and understanding.
4. Lack of Accountability and Transparency: When LLM-based evaluators are used, it can create a black-box scenario where the evaluation process itself becomes opaque and difficult to scrutinize for potential biases or errors.
Ensuring Human Oversight and Judgment:
1. Human-in-the-Loop Evaluation: Integrate human evaluation as a core component of the assessment process. This can involve tasks like qualitative analysis of generated text, assessment of bias and fairness, and evaluation of aspects that require subjective judgment.
2. Diverse Evaluation Panels: Ensure that human evaluation panels are diverse in terms of backgrounds, perspectives, and expertise to mitigate the risk of individual biases influencing the evaluation.
3. Transparent and Explainable Metrics: Develop and use automated metrics that are transparent and explainable. This allows for better understanding of what the metric is measuring and how it aligns with human judgment.
4. Ongoing Critical Reflection: Continuously reflect on the limitations of both automated and human evaluation methods. Encourage open discussion and debate about the ethical implications of different evaluation approaches.
5. Value Alignment: Prioritize the development of LLMs that are aligned with human values. This requires incorporating ethical considerations into all stages of the LLM lifecycle, from data selection and model training to evaluation and deployment.
By integrating human oversight and judgment into the evaluation process, we can ensure that LLMs are developed and assessed not just for their technical capabilities but also for their alignment with human values, promoting fairness, transparency, and accountability in the field of artificial intelligence.