Everything You Need to Know About HELM — The Stanford Holistic Evaluation of Language Models

In an era where large language models (LLMs) are becoming foundational to software, business, and even decision-making itself, understanding how these models perform under real-world conditions has never been more critical. With models like OpenAI’s GPT-4, Anthropic’s Claude, Google’s Gemini, and Meta’s LLaMA evolving at breakneck speed, the AI ecosystem is booming — but so is the complexity of evaluating these powerful tools.

Ask ten teams what “best” means in an LLM, and you’ll likely get ten different answers. Some care about accuracy, others about bias. Some prioritize speed and efficiency, while others demand interpretability or safety. The truth is, no single benchmark can capture the full picture. That’s exactly why Stanford’s HELM — Holistic Evaluation of Language Models — has emerged as a much-needed game changer.

HELM is not just another leaderboard. It’s a comprehensive, transparent, and scenario-rich framework designed to evaluate language models across 42 different real-world scenarios and 7 critical metrics, from accuracy to fairness and toxicity. Whether you’re a developer choosing a model for your app or a policymaker navigating AI governance, HELM offers a 360° lens on model behavior.

In this guide, we’ll break down what HELM is, how it works, what insights it offers, and why it matters. If you’re building, buying, or benchmarking LLMs, consider this your essential reading.

II. What Is HELM?

HELM stands for Holistic Evaluation of Language Models, a project developed by the Stanford Center for Research on Foundation Models (CRFM) to address a growing challenge in the AI landscape: the fragmented, incomplete, and often opaque ways we evaluate large language models.

Traditionally, LLMs have been assessed using narrow benchmarks like SuperGLUE (for natural language understanding), HumanEval (for coding tasks), or MMLU (for academic question answering). While useful, these benchmarks offer only a slice of a model’s capability — and often miss the bigger picture.

HELM was created to change that. It takes a holistic approach to evaluation by considering a wide range of model capabilities, failure modes, and application contexts. Rather than merely focusing on performance in isolated tasks, HELM evaluates how models behave across diverse, realistic scenarios using multiple metrics that extend beyond simple accuracy.

Key aspects that set HELM apart:

• Breadth: HELM currently evaluates 30 different language models, from industry leaders like OpenAI’s GPT-4 and Claude 3 to open models such as Meta’s LLaMA and Mistral.
• Diversity: The evaluation spans 42 scenarios, reflecting real-world use cases such as summarization, multilingual communication, dialogue generation, classification, and more.
• Depth: Instead of focusing only on whether a model gets the “right” answer, HELM investigates how confident, efficient, fair, robust, and transparent a model is.

HELM is also open and extensible. It encourages contributions from the research community and strives for reproducibility by documenting prompt formats, datasets, and model access methods. That transparency makes HELM not just a static benchmark but a living ecosystem designed to evolve with the models it evaluates.

Ultimately, HELM is an answer to a core question in AI: How can we meaningfully compare models in a world where performance is no longer one-dimensional?

III. Why Traditional Evaluation Falls Short

As LLMs proliferate and become embedded into everything from customer service bots to enterprise workflows, evaluating them has grown into a multi-dimensional challenge. However, most of the commonly used benchmarks weren’t built for this complexity — and that’s a problem.

Let’s look at a few popular benchmarks:

• SuperGLUE focuses on natural language understanding tasks like textual entailment and coreference resolution.
• HumanEval assesses coding ability by checking whether a model can generate correct Python functions.
• MMLU (Massive Multitask Language Understanding) tests a model’s knowledge across academic subjects.

While each of these offers valuable insights, they share one major limitation: they’re narrow and one-dimensional.

Here’s why traditional evaluation methods fall short in today’s LLM landscape:

1. Single-Metric Focus

Most benchmarks prioritize one outcome — typically accuracy or task completion rate. But real-world performance is far more nuanced. A model that’s accurate but slow, biased, or unpredictable may not be viable in production.

2. Limited Scenario Coverage

Benchmarks often center around artificial or academic tasks. They don’t represent the wide array of contexts in which LLMs operate: legal advice, customer support, health information, multilingual dialogue, and so on.

3. Neglect of Risks and Ethical Dimensions

Standard benchmarks rarely test for fairness, toxicity, hallucinations, or transparency. That’s like reviewing a car based solely on top speed, ignoring safety, fuel efficiency, or repair history.

4. Opaque and Inconsistent Evaluation Methods

Some benchmarks use custom datasets or closed evaluation environments, making it hard to reproduce results or compare models on equal footing.

In a rapidly evolving AI world, relying solely on outdated metrics is like using a thermometer to measure the health of an entire body. You’ll miss the full diagnosis — and that’s where HELM comes in, offering a multi-metric, multi-scenario, and fully transparent framework for evaluation.

IV. HELM’s Evaluation Framework: The Anatomy

At the heart of HELM is a robust and methodical framework designed to reflect the real-world complexity of language model usage. Instead of measuring just performance on isolated tasks, HELM evaluates how a model behaves across a wide range of tasks, risk profiles, and usage scenarios.

Let’s break it down into three key components: Models, Scenarios, and Metrics.

a. Models Evaluated

HELM currently benchmarks 30 major LLMs, spanning both proprietary and open-source ecosystems. This includes:

• Proprietary models: GPT-4, GPT-3.5 (OpenAI), Claude 2 and 3 (Anthropic), Gemini (Google DeepMind), Command R (Cohere), Jurassic-2 (AI21).
• Open-source models: LLaMA (Meta), Mistral, Falcon, OPT, T5, BLOOM, and more.

What makes HELM unique is that it doesn’t just test one model from one lab — it pits a diverse set of models against the same scenarios, using consistent methodology. This level playing field is critical to making apples-to-apples comparisons in a fragmented landscape.

b. Scenarios

Rather than evaluate models on a few academic tasks, HELM tests them across 42 real-world scenarios — each designed to reflect practical, domain-relevant use cases. These include:

• Question Answering: Answering factual or open-ended questions.
• Summarization: Condensing long documents into concise summaries
• Classification: Tagging or labeling content across categories
• Information Extraction: Pulling structured data from unstructured text
• Dialogue and Chat: Handling multi-turn conversational flows
• Multilingual Tasks: Operating across languages like Spanish, Arabic, Swahili, and more.
• Sensitive/Ethical Prompts: Testing responses in morally complex, biased, or adversarial settings

This range helps expose model strengths and blind spots. For instance, a model might excel in English summarization but underperform in multilingual QA or ethical reasoning.

c. Metrics Used: The 7 Pillars

HELM doesn’t believe in one-size-fits-all metrics. It evaluates each model across seven distinct dimensions, providing a multifaceted profile of model performance:

Each metric is critical depending on your context. A healthcare app may prioritize accuracy and fairness, while a chatbot at scale may care more about efficiency and toxicity.

By combining these three pillars — models, scenarios, and metrics — HELM doesn’t just benchmark language models. It diagnoses them across functional, ethical, and operational dimensions. That’s what makes it holistic.

V. A Closer Look at the 7 Key Metrics

HELM’s most powerful contribution isn’t just its coverage of models and scenarios — it’s how it evaluates them. Instead of treating models as black boxes judged only by accuracy, HELM shines a spotlight on seven interlocking metrics that reveal both capabilities and risks.

Let’s unpack each of these metrics with explanations, implications, and examples.

1. Accuracy

What it measures: The degree to which a model gives the correct output — typically evaluated using reference answers or expected behavior.

Accuracy still matters. Whether summarizing legal documents or answering a medical query, LLMs must get the facts right. HELM tests accuracy across QA, summarization, classification, and more.

💡 Example: In a scenario testing climate-related misinformation, GPT-4 may achieve 90% accuracy, while smaller open-source models like LLaMA-2–13B may trail at 75%.

2. Calibration

What it measures: Whether a model’s confidence in its answers matches the actual correctness of those answers.

Why it matters: A poorly calibrated model might sound very sure when it’s wrong — leading users to trust hallucinations. HELM uses confidence-based metrics like Brier score to evaluate this.

💡 Example: Claude 3 often provides balanced, cautious answers — showing stronger calibration than GPT-3.5, which may overcommit.

3. Robustness

What it measures: How sensitive a model is to slight changes in phrasing, input, or structure.

A model is considered robust if its output remains stable and correct even when the input is tweaked or paraphrased. Robustness is key for maintaining reliability in noisy, unpredictable environments.

💡 Example: In paraphrased QA scenarios, Mistral models showed a steeper drop in accuracy compared to GPT-4, highlighting fragility in open-weight systems.

4. Fairness

What it measures: The degree to which a model avoids biased or discriminatory responses based on race, gender, nationality, or other demographics.

Fairness in LLMs isn’t just an ethical concern — it’s a legal and reputational one. HELM evaluates fairness using targeted prompts designed to surface demographic bias.

💡 Example: When asked about “best leaders in history” across cultural contexts, some models displayed Western-centric bias. Claude 3 scored higher than others in generating diverse and inclusive answers.

5. Toxicity

What it measures: The likelihood that a model will generate offensive, harmful, or violent language.

HELM uses tools like Perspective API and curated prompts to test for both explicit and implicit toxicity. This metric is vital for safety in consumer-facing applications.

💡 Example: GPT-3.5 showed higher toxic responses in adversarial prompts compared to GPT-4, which included reinforced safety layers.

6. Efficiency

What it measures: Latency, token usage, and overall computational cost of model outputs.

This isn’t just about cost — it’s about scalability. A model that’s accurate but bloated in token usage may be impractical at production scale.

💡 Example: Command R+ by Cohere may be less accurate than GPT-4 but delivers faster responses with significantly fewer tokens — crucial for real-time deployments.

7. Transparency

What it measures: The extent to which the model, its training data, and evaluation processes are documented, open-sourced, and reproducible.

Transparency builds trust. HELM scores models on how open their creators are about datasets, training procedures, architecture, and safety mitigations.

💡 Example: Open models like Mistral, Falcon, and LLaMA score high on transparency, while closed models like Gemini or Claude offer limited documentation.

Each metric reveals a different aspect of a model’s character. HELM’s brilliance lies in making the trade-offs visible. A model that’s highly accurate may still be toxic. One that’s fair may be less efficient. No model scores perfectly across the board — and that’s the point.

VI. Key Insights from HELM Benchmark Results

Once you dive into HELM’s dashboard, a few things become clear: no single model dominates across every metric or scenario. Instead, each model exhibits unique trade-offs. This nuanced perspective is precisely what makes HELM such a valuable tool for decision-makers.

Let’s explore some of the most revealing insights that emerge when you analyze HELM’s evaluations of 30 LLMs across 42 scenarios and 7 key metrics.

1. There Is No “Best” Model — Only the Best Fit for the Task

HELM makes it clear that top performance is context-dependent.

• GPT-4, for instance, consistently ranks high on accuracy, calibration, and robustness, making it a strong all-rounder for high-stakes tasks.
• Claude 3 excels in fairness and safety, often producing less toxic and more ethically balanced outputs.
• Command R+ and Gemini 1.5 Pro show impressive efficiency, making them more suitable for real-time, low-latency environments.

💡 Key takeaway: Picking an LLM isn’t just about leaderboard rankings — it’s about aligning strengths to specific use cases.

2. Open Models Are Catching Up, But Trade-Offs Persist

Open-source models like Mistral, LLaMA 2, and Falcon are improving rapidly — especially in efficiency and transparency. However, they still lag behind proprietary models in tasks involving nuanced reasoning or multilingual robustness.

• LLaMA 2–13B, for example, performs competitively on summarization and QA but struggles in multilingual or sensitive contexts.
• Mistral 7B is highly efficient, but less accurate in complex dialogue tasks compared to Claude 3 or GPT-4.

💡 Key takeaway: Open models are viable for many applications — especially when transparency and customization are priorities — but may need fine-tuning or augmentation for specialized tasks.

3. Toxicity and Fairness Remain Big Challenges — Even for Top Models

Despite advances in safety alignment, bias and toxicity still persist — especially under adversarial prompts.

• GPT-3.5 tends to be more vulnerable to toxicity than its successors.
• Some open models have not implemented safety fine-tuning, leading to higher toxicity scores.

💡 Key takeaway: Don’t assume “bigger” means “safer.” Safety must be verified — and often supplemented — regardless of vendor.

4. Efficiency vs. Performance Is a Constant Trade-off

HELM reveals that performance often comes at the cost of speed and token usage.

• Claude 3 Opus and GPT-4 are accurate but token-heavy.
• Command R+ and Mistral are lighter and faster, though slightly less robust on complex reasoning.

💡 Key takeaway: If latency or API cost is a constraint, efficiency-focused models may offer a better ROI — especially for non-critical tasks.

5. Transparency Is Still a Rare Commodity

Most proprietary models score poorly on transparency. The industry is still hesitant to fully disclose training data, safety mechanisms, or even architecture details.

• Open-source models like Mistral and Falcon scored top marks here.
• Closed models like Gemini and Claude provide little to no public documentation beyond model cards.

💡 Key takeaway: For regulatory compliance and research reproducibility, transparency isn’t optional — it’s essential.

VII. Why HELM Matters for Developers, Enterprises, and Policymakers

HELM is more than just an academic benchmark — it’s a strategic tool. By revealing nuanced insights about model performance, safety, and limitations, HELM helps different stakeholders make smarter, more responsible decisions about how to use, deploy, and regulate language models.

Let’s break this down by audience.

1. For Developers: Choosing and Fine-Tuning the Right Model

If you’re building apps powered by LLMs, HELM is like a cheat sheet for informed model selection.

• Need speed and cost-efficiency? Look for models with strong efficiency metrics (e.g., Command R+, Mistral).
• Building a multilingual chatbot? Prioritize models with high robustness and accuracy across multilingual scenarios.
• Working on applications involving sensitive topics? Lean on models scoring better in fairness and toxicity dimensions.

💡 HELM helps avoid guesswork by offering transparent, side-by-side model comparisons across real-world tasks.

2. For Enterprises: Mitigating Risk and Ensuring ROI

Large organizations face unique challenges: compliance, brand safety, operational efficiency, and cost control.

HELM helps enterprises:

• Quantify trade-offs between accuracy, efficiency, and safety — essential for aligning AI choices with business KPIs.
• Avoid hidden costs by understanding how token usage and latency vary across models.
• Build trust with stakeholders by choosing models that are well-calibrated and fair — especially in regulated industries like finance, healthcare, or legal.

💡 Example: A bank adopting GenAI for customer service may prefer Claude 3 over GPT-4 if fairness and safety matter more than marginal accuracy gains.

3. For Policymakers and Regulators: Benchmarking AI Accountability

HELM’s transparency-first design makes it a critical tool for governance and oversight.

• It enables evidence-based regulation — grounded in concrete data on model behavior, bias, and safety.
• It supports algorithmic accountability by making opaque systems more inspectable.
• It encourages vendors to adopt responsible disclosures about model training, performance, and failure modes.

💡 HELM aligns closely with global efforts — like the EU AI Act and the U.S. AI Bill of Rights — to ensure that AI systems are safe, fair, and transparent.

HELM isn’t just another benchmark — it’s a mirror that reflects the true shape of a model, warts and all. Whether you’re a developer prototyping an app, a CTO planning enterprise AI adoption, or a regulator designing policy, HELM empowers better decisions with better data.

VIII. Limitations of HELM and Areas for Improvement

While HELM sets a new gold standard for evaluating language models, it’s not perfect. Like any benchmark, it has boundaries — technical, methodological, and practical. Understanding these limitations helps interpret HELM results more wisely and anticipate what future iterations might address.

1. Limited Access to Proprietary Model Internals

HELM evaluates both open and closed-source models, but it can’t peek under the hood of proprietary ones like GPT-4, Claude, or Gemini.

• There’s no visibility into exact training data, architectural tweaks, or fine-tuning processes.
• This makes transparency scoring difficult — and could skew comparisons when open models are penalized for being honest while closed ones remain vague.

💡 Improvement Needed: Encourage model providers to adopt standardized transparency disclosures or third-party audits.

2. Evaluation Metrics Are Still Evolving

Metrics like toxicity, fairness, and calibration are highly contextual — and often hard to quantify reliably.

• For instance, the fairness of a model’s response to a cultural prompt may depend on who is evaluating it, and from what perspective.
• Toxicity detection tools (e.g., Perspective API) may miss subtle harmful patterns or flag benign content in certain dialects.

💡 Improvement Needed: Complement quantitative scores with human-in-the-loop evaluation and context-aware benchmarks.

3. Benchmarking Is Static — But Models Evolve Constantly

HELM provides a snapshot in time, but LLMs are evolving rapidly with ongoing fine-tuning, safety patches, and API updates.

• A model evaluated in January 2024 might behave very differently in June 2025.
• This leads to benchmark drift — where scores quickly become outdated.

💡 Improvement Needed: Move toward continuous or periodic evaluation cycles, with version tracking and changelogs.

4. Coverage Gaps in Emerging Use Cases

While HELM’s 42 scenarios are broad, some real-world tasks and modalities remain underrepresented:

• Vision-language tasks (e.g., image captioning or document QA with diagrams)
• Multimodal agents and tool-using LLMs
• Enterprise-grade tasks like retrieval-augmented generation (RAG), legal contract parsing, or scientific paper summarization

💡 Improvement Needed: Expand HELM into multimodal and agentic benchmarks, incorporating plug-in and tool-use capabilities.

5. Generalization vs Specialization

HELM rewards general-purpose performance, but some models are domain-specialized (e.g., Med-PaLM for healthcare, BloombergGPT for finance).

• These models might underperform on HELM but outperform in narrow domains.
• HELM doesn’t currently adapt to niche benchmark suites or vertical expertise.

💡 Improvement Needed: Support domain-specific extensions of HELM for medicine, law, finance, and more.

Summary: Limitations Aren’t Failures — They’re Roadmaps

HELM is already a major leap forward in model evaluation, but its limitations point to where the ecosystem must grow:

• From static to continuous benchmarking
• From language-only to multimodal
• From model-agnostic to domain-aware
• From surface metrics to deep accountability

These are not weaknesses — they’re next chapters waiting to be written.

IX. The Future of HELM and Holistic Model Evaluation

HELM isn’t just a benchmark — it’s a blueprint for how we evaluate the language models that increasingly shape our digital lives. As the frontier of AI continues to shift, HELM’s holistic, transparent, and comparative approach lays the foundation for a new era of trustworthy AI deployment.

Here’s what the future might hold — for HELM and the broader evaluation landscape.

1. Continuous and Real-Time Evaluation

As LLMs update weekly or even daily, static benchmarking becomes obsolete. The future lies in continuous evaluation pipelines.

• HELM could evolve to support real-time model monitoring, showing how a model’s behavior shifts with new training runs, fine-tuning, or policy updates.
• Think “CI/CD for AI evaluation” — where updates are instantly benchmarked, compared, and published.

💡 Impact: Enterprises will be able to track drift and degradation over time, preventing silent failures in production.

2. Expansion into Multimodal and Agentic Capabilities

With models like GPT-4o, Gemini, and Claude 3 gaining vision and tool-use abilities, HELM will need to go beyond text.

• Upcoming HELM versions may incorporate multimodal tasks (image + text), tool use, external API calls, and even simulation environments for agent behavior.
• This would reflect the real-world complexity of apps that rely on LLM agents interacting with documents, databases, or visual data.

💡 Impact: Evaluation will move from “can the model answer this?” to “can the model solve this end-to-end task, responsibly and efficiently?”

3. Deeper Focus on Human-Centric and Ethical Metrics

Metrics like fairness and toxicity are just the beginning. The future of holistic evaluation will include:

• Intent alignment: Does the model truly understand what the user wants?
• Emotional resonance: Does the output support empathy or degrade it?
• Societal impact forecasting: What downstream effects could this model have?

These next-gen metrics will require multi-disciplinary collaboration — bringing in ethicists, social scientists, and UX researchers.

💡 Impact: AI becomes more than just performant — it becomes accountable, inclusive, and aligned with human values.

4. Industry-Wide Standardization and Adoption

Today, HELM is still mostly used by researchers and leading AI labs. Tomorrow, it could be the ISO or IEEE of AI benchmarking.

• Standardized HELM scores could appear in model cards, vendor disclosures, or even government compliance checklists.
• Enterprises and regulators alike could refer to HELM scores as part of due diligence, procurement, and auditing workflows.

💡 Impact: A more mature AI ecosystem — where transparency is built in, not bolted on.

5. Open Participation and Community Contributions

The HELM team at Stanford has already invited feedback — but what if it became a collaborative open science project?

• Researchers could contribute new metrics and scenarios.
• Enterprises could share anonymized model behaviors from production environments.
• Governments and civil society groups could co-design benchmarks that reflect societal needs and risks.

💡 Impact: A democratized benchmarking process that stays agile, inclusive, and globally relevant.

Final Thoughts

The rise of LLMs has been nothing short of revolutionary. But revolutions need guide rails. HELM provides those — balancing power with responsibility, performance with safety, and innovation with ethics.

If you’re building, buying, deploying, or regulating large language models, you can’t afford to ignore HELM. It’s not just about which model is best — it’s about which model is best for your task, your users, and your values.