What makes a good LLM eval?

Spring Break is finally here and I wanted to do some research on something that's been on my mind for the past month, ever since Dwarkesh Patel's Podcast episode with Sholto Douglas and Trenton Bricken. I wanted to have fun and make my own benchmark; just for kicks, without overly complex strategies. Just make something abstract that no one has thought about yet, like evaluating an LLM on their ability to come up with convincing philosophical claims in a few-shot environment. But I ended up realizing that there is not much blog posts/videos/condensed sources on what even makes a good eval. So, this blog summarizes my findings.

There is a lot of discussons and an increasing need for more comphrehensive benchmarks that evaluate the capabilities of continuously more sophisticated models we use today. Subsequently, the shortcomings of current benchmarks, particularly what they evaluate, how they do so and how well they stay relevant to testing future models calls for a re-evaluation of our benchmarking strategies, incorporating novel metrics that reflect the complexity of modern AI systems. This would involve developing more nuanced evaluation frameworks that account for the unique strengths and weaknesses of contemporary models, such as their ability to generalize, adapt to new situations, and operate in uncertain environments. By doing so, we can ensure that future benchmarks accurately assess the capabilities of increasingly sophisticated AI models, ultimately driving progress towards more effective, efficient, and reliable artificial intelligence systems.

Unfortunately this is still a developing subset of the AI field. There is much talk around it, not much action and innovation. Funny to use the word innovation in regards to a testing problem.

I recently saw a cool little Jupyter Notebook that used Claude 3 and a Google Search API to generate reports on a topic. After generating queries, it makes loop runs, summarizing information it found, to then critique it and fill in the gaps by making another round of queries, with all the previous context consequently fed back into it. I've been in love with Opus and have switched from GPT4 on the day of its release, but was not aware of how good it actually is at tasks such as this one.

This is the final report of what the AI-researcher was able to find. At this point, the input token count reached 90,000+. You could say I spent some ($1) money on this blog.

Query: I want to create a new evaluation benchmark for large language models. What are some benchmarks that people in the AI scene are hoping to see? What makes a great benchmark?

Key Characteristics of Effective Evaluation Benchmarks

Introduction

Evaluation benchmarks are an essential component of any effective evaluation system. They serve as standards or measures against which the performance, quality, or progress of an intervention, program, or system can be assessed. Effective evaluation benchmarks possess several key characteristics that contribute to their utility and reliability. This report will explore these essential characteristics in detail.

Key Characteristics of Effective Evaluation Benchmarks

1. Relevance

Effective evaluation benchmarks are closely aligned with the objectives, activities, and intended outcomes of the intervention or program being evaluated. They directly measure the key indicators of success that are most meaningful and important.

2. Reliability

Evaluation benchmarks should produce consistent, stable, and dependable results over time. The measurement process should be standardized and replicable, minimizing the influence of external factors or subjective biases.

3. Validity

Benchmarks must accurately and comprehensively capture the construct or phenomenon they are intended to measure. They should have a strong theoretical and empirical basis, and their relationship to the underlying concepts should be well-established.

Approaches for Empirically Validating the Construct Validity of Evaluation Benchmarks

The additional search results provide insights on various approaches for empirically validating the construct validity of evaluation benchmarks:

Convergent Validity: Examine the extent to which the benchmark correlates with other measures that are theoretically related to the same construct.
Discriminant Validity: Assess the degree to which the benchmark is distinct from measures of unrelated constructs, demonstrating that it is not simply measuring something else.
Nomological Validity: Evaluate whether the benchmark's relationships with other variables conform to theoretical predictions, providing evidence for the underlying construct.
Factor Analysis: Use statistical techniques, such as exploratory or confirmatory factor analysis, to examine the benchmark's factor structure and its alignment with the theoretical construct.
Criterion-Related Validity: Investigate how well the benchmark predicts or is related to a relevant criterion, such as performance outcomes or other established measures.
Longitudinal Studies: Conduct longitudinal research to assess the stability of the benchmark's relationships over time, further supporting its construct validity.
Experimental Manipulation: Employ experimental designs to manipulate the construct and observe the benchmark's sensitivity to these changes, providing evidence of its construct validity.

By incorporating these empirical validation approaches, evaluation teams can build a robust case for the construct validity of their evaluation benchmarks, enhancing their credibility and usefulness.

4. Specificity

Effective benchmarks are clearly defined, with unambiguous criteria and operational definitions. This ensures that the measurement process is transparent and that all stakeholders have a shared understanding of what is being assessed.

5. Measurability

Benchmarks should be quantifiable, with clear targets or thresholds that can be objectively observed and evaluated. This allows for the tracking of progress and the identification of areas for improvement.

6. Timeliness

Benchmarks should be established and measured at appropriate intervals, aligned with the timeline of the intervention or program. This ensures that the evaluation data is available when needed to inform decision-making and program adjustments.

7. Feasibility

The data required to assess the benchmarks should be accessible, affordable, and practical to collect. The evaluation process should not impose an undue burden on program staff or participants.

8. Stakeholder Engagement

The development and selection of evaluation benchmarks should involve key stakeholders, including program participants, staff, and decision-makers. This fosters buy-in, ensures the relevance of the benchmarks, and promotes the use of the evaluation findings.

Strategies for Aligning Evaluation Benchmarks with Program Timelines and Measurement Intervals

The additional search results provide insights on strategies for aligning evaluation benchmarks with program timelines and measurement intervals:

Prioritize Key Performance Indicators: Focus on the most critical and relevant performance indicators that closely align with the program's strategic goals and objectives. This helps ensure that the evaluation is focused and efficiently uses available resources.
Establish Measurement Schedules: Develop a clear schedule for when each evaluation benchmark will be measured, ensuring that the data collection is timed to coincide with key program milestones and decision-making points.
Ensure Timely Data Availability: Design the evaluation plan to ensure that the benchmark data is collected and analyzed in a timely manner, so that the findings can be used to inform program adjustments and future decision-making.
Leverage Existing Data Sources: Whenever possible, utilize existing data sources and reporting mechanisms to collect the necessary information for the evaluation benchmarks. This can help minimize the burden on program staff and participants.
Adopt a Phased Approach: Consider implementing the evaluation in phases, with a focus on the most critical benchmarks in the early stages. This allows for the collection of essential data while also providing opportunities to refine the evaluation approach over time.
Build in Feedback Loops: Incorporate regular check-ins with stakeholders to review the alignment of the benchmarks with the program timeline and make any necessary adjustments. This helps ensure that the evaluation remains responsive to the program's evolving needs.
Leverage Technology: Utilize technology-enabled tools, such as dashboards and automated reporting systems, to streamline the data collection, analysis, and dissemination processes. This can enhance the timeliness and efficiency of the evaluation.

By employing these strategies, evaluation teams can better align their evaluation benchmarks with the program's timeline and measurement intervals, ensuring that the evaluation data is available when needed to inform decision-making and program improvements.

Challenges and Pitfalls in Designing Evaluation Benchmarks

The additional search results also highlight some common challenges and pitfalls to avoid when designing evaluation benchmarks:

Lack of Clear Objectives: Failing to define clear and specific objectives for the evaluation can lead to the selection of inappropriate or irrelevant benchmarks.
Choosing the Wrong Benchmarks: Selecting benchmarks that do not accurately reflect the desired outcomes or performance indicators can undermine the validity and usefulness of the evaluation.
Benchmarking Too Many Indicators: Trying to track too many key performance indicators (KPIs) can result in an overwhelming and unfocused evaluation process.
Overreliance on Historical Data: Benchmarking primarily based on past performance data can overlook important contextual factors and future trends, leading to suboptimal decision-making.
Insufficient Stakeholder Involvement: Failing to engage key stakeholders in the benchmark development process can result in a lack of buy-in, relevance, and ultimately, the use of the evaluation findings.
Measurement Issues: Challenges in data collection, analysis, and interpretation can undermine the reliability and validity of the benchmarks.
Lack of Feedback and Iteration: Not seeking feedback from stakeholders or failing to update the benchmarks based on new information can lead to outdated or ineffective evaluation tools.

To address these challenges, evaluation teams should:

Clearly define the evaluation objectives and align the benchmarks accordingly.
Carefully select a manageable number of the most relevant and meaningful benchmarks.
Incorporate both historical and forward-looking data to inform the benchmark development.
Actively engage stakeholders throughout the benchmark design and implementation process.
Ensure the reliability and validity of the data collection and analysis methods.
Establish feedback loops and regularly review and update the benchmarks as needed.

By proactively addressing these common pitfalls, evaluation teams can develop more effective and impactful evaluation benchmarks.

Practical Considerations and Resource Implications for Developing and Implementing Effective Evaluation Benchmarks

The additional search results provide further insights on the practical considerations and resource implications for developing and implementing effective evaluation benchmarks:

Evaluation Criteria: Clearly define the evaluation criteria, such as relevance, effectiveness, efficiency, and sustainability, that will guide the development and selection of the benchmarks. Align these criteria with the program's objectives and stakeholder needs.
Resource Allocation: Assess the available resources, including funding, staffing, and time, to ensure that the development and implementation of the evaluation benchmarks are feasible and sustainable. Allocate resources accordingly to support the evaluation process.
Data Collection and Management: Determine the data required to measure the evaluation benchmarks and ensure that the necessary data collection methods, tools, and systems are in place. Invest in appropriate technology and data management infrastructure to facilitate efficient data collection, storage, and analysis.
Stakeholder Engagement: Engage key stakeholders, including program participants, staff, and decision-makers, throughout the benchmark development and implementation process. Allocate resources for stakeholder communication, capacity building, and feedback mechanisms to foster buy-in and ownership.
Benchmark Refinement: Establish processes for regularly reviewing and updating the evaluation benchmarks to ensure their continued relevance and responsiveness to evolving program needs and contextual changes. Allocate resources for ongoing benchmark refinement and adaptation.
Capacity Building: Invest in the training and development of the evaluation team to ensure they have the necessary skills and expertise to design, implement, and interpret the evaluation benchmarks effectively. This may include training on data analysis, stakeholder engagement, and evaluation methodologies.
Dissemination and Utilization: Allocate resources for the dissemination of the evaluation findings and the integration of the benchmark data into decision-making processes. This may involve developing communication strategies, data visualization tools, and feedback mechanisms to ensure the evaluation insights are used to inform program improvements and strategic planning.

By carefully considering these practical considerations and resource implications, evaluation teams can develop and implement evaluation benchmarks that are robust, sustainable, and effectively support program improvement and decision-making.

Conclusion

Effective evaluation benchmarks are essential for measuring the success and impact of interventions and programs. By possessing the key characteristics of relevance, reliability, validity, specificity, measurability, timeliness, feasibility, and stakeholder engagement, as well as incorporating best practices for stakeholder involvement and addressing common challenges, evaluation benchmarks can provide robust, meaningful, and actionable data to inform decision-making and drive continuous improvement.

Furthermore, by aligning evaluation benchmarks with program timelines and measurement intervals through strategies such as prioritizing key performance indicators, establishing measurement schedules, ensuring timely data availability, leveraging existing data sources, adopting a phased approach, building in feedback loops, and utilizing technology, evaluation teams can enhance the relevance, timeliness, and utility of the evaluation findings.

Lastly, by considering the practical considerations and resource implications, such as defining evaluation criteria, allocating resources, managing data, engaging stakeholders, refining benchmarks, building capacity, and facilitating dissemination and utilization, evaluation teams can develop and implement effective evaluation benchmarks that are sustainable and well-integrated into the program's decision-making processes.

Potential Novel Benchmarks for Assessing Language Model Capabilities

Introduction

The rapid advancements in large language models (LLMs) have led to a growing need for comprehensive and innovative benchmarks to assess their capabilities. Existing benchmarks, while valuable, may not fully capture the evolving capabilities of these models. This report explores potential novel benchmarks that could provide a more holistic and nuanced evaluation of language model performance.

Emerging Benchmarks and Frameworks

Plausibility Ranking Evaluation for Language Models (PRobELM): This benchmark is designed to evaluate language models' ability to discern plausible scenarios, filling a critical gap in current evaluation methods. PRobELM focuses on assessing the model's understanding of real-world coherence and causal reasoning.
Language-Model-as-an-Examiner (LME): This novel benchmarking framework leverages the capabilities of language models themselves to evaluate other foundation models. The key idea is to use a language model as an "examiner" to assess the performance of other models on a variety of tasks.
NovelQA: This benchmark is focused on long-range novel question answering, aiming to assess language models' ability to comprehend and reason over long-form text. It features manually crafted questions, golden answers, and evidence, providing a more challenging and realistic evaluation.
API-Bank: This benchmark evaluates LLMs' decision-making in API calls, their proficiency in understanding and following API documentation, and their ability to generate correct API-based code. It assesses the practical, task-oriented capabilities of language models.
BenchCLAMP: This benchmark covers nine parsing datasets with seven different parsing tasks, evaluating language models' abilities in constrained language model parsing, a crucial skill for practical applications.
Holistic Evaluation of Language Models (HELM): HELM is a comprehensive benchmark that aims to provide broad coverage and recognize the multifaceted nature of language models. It includes a diverse set of tasks and metrics to assess various aspects of language model performance.

Advantages of Novel Benchmarks

Broader Evaluation Scope: Novel benchmarks like PRobELM, LME, and NovelQA expand the evaluation scope beyond traditional natural language processing (NLP) tasks, capturing more nuanced and practical capabilities of language models.
Increased Realism and Complexity: Benchmarks like NovelQA and BenchCLAMP introduce more realistic and complex tasks, better reflecting the real-world challenges language models may face in various applications.
Improved Interpretability: Frameworks like LME and HELM aim to provide greater interpretability and transparency in the evaluation process, enabling a deeper understanding of language model strengths and weaknesses.
Adaptability to Emerging Capabilities: Novel benchmarks are designed to be more flexible and adaptable, allowing them to evolve alongside the rapid advancements in language model capabilities.

Conclusion

As language models continue to advance, it is crucial to develop innovative benchmarks that can accurately and comprehensively assess their capabilities. The potential novel benchmarks discussed in this report offer promising avenues for a more holistic evaluation of language models, paving the way for their more informed and responsible deployment in various applications.