The Ultimate Guide to LLM Leaderboards: Part 2
As the demand for artificial intelligence capabilities continues to grow, the importance of reliable and comprehensive leaderboards will only increase.
Hi ! 👋  I'm KP. Welcome to my weekly dive into the ever-evolving world of artificial intelligence. Each week, I curate and break down the most impactful AI news and developments to help tech enthusiasts and business professionals like you stay ahead of the curve.
In part 1, we explored five Large Language Model (LLM) scoreboards in the evaluation arena:
The Open LLM Leaderboard v2
LMSYS Chatbot Arena Leaderboard
Berkeley Function-Calling Leaderboard (BFCL)
Massive Text Embedding Benchmark (MTEB) Leaderboard
Hughes Hallucination Evaluation Model (HHEM) leaderboard Leaderboard
Let’s continue our last week’s journey… Round two of LLM leaderboards where we'll uncover new leaderboards and continue to analyze their strengths, limitations, and practical implications for businesses.
SEAL (Safety, Evaluations, and Alignment Lab) Leaderboard
The SEAL leaderboard, developed by Scale AI, uses private and curated datasets to rank frontier LLMs. It focuses on providing unbiased, tamper-proof rankings across multiple domains, including coding, instruction following, mathematics, and multilingual capabilities. These were released in May 2024 as the first truly expert-driven evaluations.
Scale AI secured $1 billion in funding during the Series F round led by Accel in May 2024, valuing the data labeling and evaluation startup at an impressive $13.8 billion.
Key Metrics and Evaluation Methodology:
Utilizes Elo-scale rankings to compare model performance and uses the Bradley-Terry model for maximum likelihood estimation
Employs human evaluators to compare responses from two models to the same prompt
Evaluations are conducted on proprietary, private datasets to prevent gaming
Scale introduced the Adversarial Robustness leaderboard as part of SEAL Leaderboards, evaluating top AI models against 1,000 adversarial prompts covering critical areas like illegal activities and hate speech. This leaderboard focuses on universally recognized harms, employs creative red teamers, uses a multi-tiered review system, and encourages community contributions to refine harm categories, aiming to advance AI safety standards industry-wide.
Strengths:
Leaderboard Integrity: Private datasets prevent exploitation or incorporation into training data
Domain Specialization: Covers multiple areas with tailored evaluation methods
Data Quality: Multi-round reviews and internal quality assurance processes for both prompts and ratings
Transparency: Publishes detailed evaluation methodologies and insights
Expert Involvement: Utilizes verified domain experts for assessments
Limitations:
Limited Public Access: Proprietary nature may limit widespread adoption or scrutiny
Potential for Bias: Despite efforts, expert evaluations may introduce some subjectivity
Evolving Methodology: As a newer leaderboard, methods may still be refining
Use Cases:
Model Selection and Capability Assessment: Helps businesses choose the most suitable LLM for specific tasks or domains while providing insights into the current state-of-the-art in various domains.
Research Direction: Identifies areas of strength and weakness in current LLMs, guiding R&D efforts. Allows companies to gauge their AI models against industry leaders.
Artificial Analysis LLM Performance Leaderboard
The Artificial Analysis LLM Performance Leaderboard is a comprehensive evaluation platform designed to assist AI engineers in selecting the most suitable LLMs and API providers for their use-cases. It uniquely combines quality, price, and speed metrics for both open and proprietary models, offering a holistic view for decision-making.
Key Metrics and Evaluation Methodology:
Quality: A simplified index based on metrics like MMLU, MT-Bench, HumanEval scores, and Chatbot Arena ranking.
Pricing: Input/output per-token pricing and blended pricing
Throughput: Token generation speed (tokens per second), reported as median and percentiles.
Latency: Time to First Token (TTFT), reported as median and percentiles.
Evaluations under various prompt lengths (100, 1k, 10k tokens) and query loads (1 and 10 parallel queries).
Strengths:
Comprehensive Metrics: Combines quality, price, and performance in a single platform.
Diverse Model Coverage: Includes both open and proprietary models.
Real-World Applicability: Tests various workloads to simulate different use cases.
Transparency: Detailed methodology available for review.
Limitations:
Rapid Field Evolution: Fast-paced LLM development may outpace leaderboard updates.
Limited Domain-Specific Evaluation: May not fully capture performance in specialized fields.
Use Cases:
Model Selection and Cost Optimization: Aids in choosing the most suitable LLM based on quality, cost, and performance requirements. Helps businesses balance model capabilities with operational costs.
API Provider Evaluation: Assists in selecting the most efficient API providers for LLM integration.
Scaling Decisions: Informs decisions on scaling AI infrastructure based on throughput and latency metrics.
Open Medical-LLM Leaderboard
The Open Medical-LLM Leaderboard is a benchmarking platform that evaluates the performance of models in medical knowledge and question answering capabilities. The leaderboard focuses on assessing the models' capabilities in medical language reasoning, generation, and understanding tasks.
Key Metrics and Evaluation Methodology:
The leaderboard utilizes a diverse set of medical datasets, including MedQA (US Medical License Exams), PubMedQA, MedMCQA, and subsets of MMLU related to medicine and biology.
It uses accuracy as its primary evaluation metric, which measures the percentage of correct answers provided by the model across various medical QA datasets.
Strengths:
Standardized Framework for Healthcare: The leaderboard aims to offer a framework specifically designed for the medical domain, addressing the unique challenges and requirements of healthcare applications.
Focus on Medical Domain: It provides a comprehensive assessment of LLMs' performance across a wide range of medical tasks and datasets, enabling a thorough understanding of their capabilities and limitations.
Limitations:
Incomplete Adaptation: Significant additional work is required to adapt these models for specific medical use cases.
Limited Scope: The leaderboard may not cover all relevant aspects of medical knowledge or specialties.
Potential Inaccuracies: The models may contain errors or biases that could be dangerous if used for medical decision-making.
Use Cases:
Medical Research and Education: Assisting researchers in exploring new hypotheses or analyzing large volumes of medical literature. Supporting the development of educational tools for medical students, professionals and general public.
Healthcare AI Development: Providing a benchmark for developers working on medical AI applications.
Medical Documentation and Information Retrieval: Aiding in the development of tools for more efficient medical record-keeping and summarization. Enhancing search capabilities for medical databases and literature.
BigCodeBench Leaderboard
BigCodeBench Leaderboard ranks LLMs evaluated on execution-based programming benchmark consisting of practical and challenging coding tasks. It focuses on assessing LLMs' ability to solve open-ended programming problems, emphasizing diverse function calls and complex instruction following.
Key Metrics and Evaluation Methodology:
Two variants: BigCodeBench-Complete (code completion) and BigCodeBench-Instruct (code generation)
The metrics for Complete and Instruct reflect the calibrated pass@1 score, which is the proportion of tasks for which the model produces a correct solution on the first attempt.
Elo rating system for BigCodeBench-Complete variant
Utilizes the bigcodebench framework for end-to-end evaluation
Strengths:
Practicality: Covers various real-world programming scenarios
Challenging Tasks: Requires strong compositional reasoning and instruction-following capabilities
Comprehensive: Assesses both code completion and generation skills
Limitations:
Language Specificity: Released in June 2024, BigCodeBench is Python-only, which limits its applicability for evaluating LLMs in other programming languages.
Potential for Saturation: As LLM capabilities rapidly improve, there's a possibility that top models may achieve high scores, necessitating future benchmark updates to maintain challenge.
Computational Requirements: The execution-based evaluation can be resource-intensive, potentially taking 1-2 hours on machines with fewer cores making it costly.
Limited Scope: The benchmark currently covers common libraries and daily programming tasks, which may not fully assess generalization to emerging tools and libraries.
Use Cases:
AI-Assisted Programming: Helps choose the most capable LLM for coding assistance and guides the development of more effective code generation tools
Software Development Workflow: Assists in integrating AI into existing development processes
LLM-Perf Leaderboard
The LLM-Perf Leaderboard focuses on benchmarking performance of LLMs across various hardware configurations, backends, and optimizations using Optimum-Benchmark. This leaderboard aims to provide a holistic view of model performance, considering both effectiveness and efficiency.
Key Metrics and Evaluation Methodology:
The leaderboard includes Prefill (the initial phase where input tokens are processed by the model to prepare for the generation of new tokens) and Decode (the process of generating new tokens, one at a time). Average evaluation score is taken from the Open LLM Leaderboard.
Three types of Memory Usage: Max Allocated Memory (PyTorch), Max Reserved Memory (PyTorch) and Max Used Memory (PyNVML)
Energy Consumption: Measured in kWh using CodeCarbon, considering GPU, CPU, RAM, and machine location
Strengths:
Multi-faceted Performance Analysis: Covers latency, throughput, memory usage, and energy consumption. Benchmarks across different hardware setups.
Energy Efficiency Focus: Includes energy consumption metrics, promoting sustainable AI development
Limitations:
Developmental Stage: The Optimum-Benchmark, which forms the foundation of the LLM-Perf Leaderboard, is currently a work in progress and not yet production-ready. While it offers valuable insights, users should be aware of its evolving nature and potential for changes or improvements.
Use Cases:
Hardware Performance Comparison: Enables hardware decision-makers to identify strengths and areas for improvement in their hardware for LLM applications. Helps users determine which hardware configurations work best with specific models, optimizing deployment strategies.
Model Performance Evaluation: Assists users in selecting the most suitable model for their specific use case, considering both quality and efficiency metrics.
Optimization and Quantization Benchmarking: Allows for the evaluation of hardware and backend-specific optimizations and quantization schemes.
This week we took a closer look at some more LLM leaderboards:
SEAL Leaderboards
Artificial Analysis LLM Performance Leaderboard
Open Medical LLM Leaderboard
BigCodeBench Leaderboard
LLM-Perf Leaderboard
We explored various platforms that evaluate the capabilities of LLMs across diverse tasks and domains. As we have seen, each leaderboard focuses on different aspects of LLM capabilities, from language understanding and code generation to minimizing hallucinations and performing domain-specific tasks. These benchmarks are crucial for advancing the field of AI by highlighting strengths and limitations, guiding future research, and setting performance standards.
However, the rapid pace of innovation in AI and machine learning means that these benchmarks must evolve. It is essential to periodically update the benchmark datasets and methodologies to maintain their relevance and avoid test set contamination. This will ensure that leaderboards continue to provide accurate and meaningful evaluations of new models and techniques.
Ultimately, leaderboards are vital tools for tracking progress and encouraging healthy competition in the development of LLMs.
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