7 LLM Benchmarks for Performance, Capabilities, and Limitations
Performance Benchmarks
1. SuperGLUE
SuperGLUE evaluates natural language understanding (NLU) through tasks like sentiment analysis, reading comprehension, and question answering.
By presenting challenges like multiple-choice questions and logical reasoning, SuperGLUE tests whether a model truly grasps language, not just at a surface level but in context. For chatbots and virtual assistants, this benchmark separates the contenders from the pretenders.
2. XTREME
XTREME tests multilingual and cross-lingual performance by evaluating tasks like document retrieval, translation, and sentiment classification. These tests reveal whether a model can adapt seamlessly across languages with different grammatical and structural rules.
For organizations operating globally, XTREME helps determine if a model can deliver consistent performance regardless of the language it's working in.
Capability Benchmarks
3. MMLU (Massive Multitask Language Understanding)
MMLU challenges models with reasoning tasks across 57 domains like humanities, STEM, and social sciences. The benchmark presents domain-specific questions that demand applied reasoning, testing whether a model can synthesize and apply knowledge rather than rely on rote patterns.
Finance: Can the model make sense of complex regulations, dissect financial scenarios, or help with risk analysis?
Healthcare: Does it understand medical research, interpret clinical guidelines, or offer insights for treatment decisions tailored to individual patients?
Education: Can it create high-quality teaching materials, assist with curriculum design, or provide precise answers to domain-specific questions from students?
Legal: How well does it navigate case law, draft legal arguments, or assist with detailed research for complex cases?
4. HellaSwag
HellaSwag focuses on commonsense reasoning, requiring models to predict the most logical continuation of a given scenario. Tasks include filling in the blanks for sentences or understanding situational context. This benchmark sharpens a model's ability to handle open-ended and user-driven queries. Applications like customer support systems or knowledge platforms benefit greatly from models that perform well here.
5. BBH (Big-Bench Hard)
BBH takes models through higher-order reasoning with ambiguous, multi-step challenges. These 23 scenarios are designed to stretch the limits of LLM capabilities, testing their ability to handle advanced problem-solving.
For example, tasks might require a model to solve intricate puzzles or derive answers from layered datasets where dependencies between inputs must be carefully navigated. It tests whether models can retain context across steps and produce coherent, logically sound outputs.
BHH's Chain-of-Thought (CoT) prompting has been shown to improve performance significantly, guiding models to produce structured, step-by-step answers.
Limitation Benchmarks
6. StereoSet
StereoSet evaluates demographic biases in a model's outputs, focusing on areas like gender, ethnicity, and cultural stereotypes. It tests whether models unintentionally reinforce or amplify harmful associations, offering a structured way to address fairness challenges.
This evaluation asks the following questions:
Does the model associate specific professions with particular genders?
Are certain ethnicities portrayed in stereotypical contexts more frequently than others?
Does the model's tone or phrasing shift depending on demographic cues in the input?
These insights are essential for building systems that meet fairness standards and regulatory requirements, especially in industries where unbiased decision-making is critical, like hiring platforms, credit assessments, or customer service AI companies.
7. TruthfulQA
TruthfulQA measures how reliably a model can generate accurate responses to over 800 complex, knowledge-driven questions while identifying instances of hallucination – responses that may appear credible but lack factual basis. It helps ensure that a model maintains credibility in knowledge-intensive applications.
For example, a hallucination might occur if a model is asked about a specific financial regulation and confidently provides a plausible-sounding explanation for a law that doesn't actually exist. In healthcare, it might invent details about a treatment protocol or cite a nonexistent clinical study, potentially leading to harmful decisions if relied upon.
Build an LLM You Can Trust with Citrusˣ
LLM benchmarks are your roadmap for deploying AI systems that are accurate, ethical, and ready for real-world challenges. They help you evaluate performance, identify risks, and fine-tune models so they deliver on their promises. Whether you're testing for reasoning, fairness, or accuracy, benchmarks give you the tools to build AI you can trust—and that your stakeholders will trust, too.
Managing AI risks is no small task, especially as industries like finance, healthcare, and insurance dive into GenAI. That's why Citrusˣ has introduced Citrusˣ RAGRails, a powerful tool designed to make AI validation easier and more reliable. RAGRails validates model accuracy (including the embedding model), proactively detects bias, and keeps your systems compliant through real-time monitoring and guardrails.
To take control of your AI initiatives and ensure they're secure, fair, and effective, become a RAGRails beta tester today to see how it can help you set a new standard for AI governance.