Seal Llm Leaderboards Expert Driven Evaluations Scale

Bonisiwe Shabane

-Jan 2, 2026, 4:26 PM

seal llm leaderboards expert driven evaluations scale

SEAL LLM Leaderboards evaluate frontier LLM capabilities. These leaderboards provide insight into models through robust datasets and precise criteria to benchmark the latest AI advancements. Gemini 2.5 Pro Experimental (March 2025) Claude 3.7 Sonnet (Thinking) (February 2025) Gemini 2.5 Pro Experimental (March 2025) Gemini 2.5 Pro Experimental (March 2025)

Scale AI has announced the launch of SEAL Leaderboards, an innovative and expert-driven ranking system for large language models (LLMs). This initiative is a product of the Safety, Evaluations, and Alignment Lab (SEAL) at Scale, which is dedicated to providing neutral, trustworthy evaluations of AI models. The SEAL Leaderboards aim to address the growing need for reliable performance comparisons as LLMs become more advanced and widely utilized. With hundreds of LLMs, comparing their performance and safety has become increasingly challenging. Scale, a trusted third-party evaluator for leading AI labs, has developed the SEAL Leaderboards to rank frontier LLMs using curated private datasets that cannot be manipulated. These evaluations are conducted by verified domain experts, ensuring the rankings are unbiased and provide a true measure of model performance.

The SEAL Leaderboards initially cover several critical domains, including: Each domain features prompt sets created from scratch by experts, tailored to evaluate performance in that specific area best. The evaluators are rigorously vetted, ensuring they possess the necessary domain-specific expertise. To maintain the integrity of the evaluations, Scale’s datasets remain private and unpublished, preventing them from being exploited or included in model training data. The SEAL Leaderboards limit entries from developers who might have accessed the specific prompt sets, ensuring unbiased results. Scale collaborates with trusted third-party organizations to review their work, adding another layer of accountability.

Nice, a serious contender to @lmsysorg in evaluating LLMs has entered the chat. LLM evals are improving, but not so long ago their state was very bleak, with qualitative experience very often disagreeing with quantitative rankings. This is because good evals are very difficult… LLM evals are the hot topic in AI right now, and the work @scale_AI is doing is helping shape the frontier! @danielxberrios @summeryue0 📣 Scale is excited to release the SEAL leaderboards today, kicking off the first truly expert-driven, trustworthy LLM contest open to all: https://lnkd.in/g4K5mdfC Compared to existing benchmarks, these leaderboards developed by our Safety, Evaluations,...

These leaderboards are regularly updated to include new models and capabilities. Our goal is to foster a culture of transparency and openness in the development and evaluation of frontier models. 👉 Finally, we are also announcing the general availability of Scale Evaluation: a platform to enable organizations to evaluate and iterate on their AI models and applications. Learn more: https://lnkd.in/g9SuEsaN 👈 Check out the leaderboard yourself here: https://lnkd.in/gffe85mg And learn more about the development and motivation behind the leaderboards: https://lnkd.in/g9W9SJH5 AI Weekly News: Stay current without the noise May 29, 2024: Scale AI Introduces LLM Performance Rankings - Scale AI launches its first SEAL Leaderboards, ranking large language models (LLMs) on performance across specific domains like coding, multilinguality, and math.

OpenAIs GPT models and Googles Gemini excel, with Anthropics Claude 3 Opus leading in math. The rankings, aimed at providing transparency in AI capabilities, derive from evaluations using private datasets and are set to update periodically, including new models and domains. Datagrom keeps business leaders up-to-date on the latest AI innovations, automation advances, policy shifts, and more, so they can make informed decisions about AI tech. The Scale AI SEAL Leaderboards are a new initiative designed to rank large language models (LLMs) based on unbiased evaluations and expert assessments. Here’s a breakdown of their key features and methodologies: 1.

**Purpose**: The SEAL Leaderboards aim to provide a trustworthy and expert-driven ranking system for LLMs. They are intended to eliminate biases that may arise from traditional evaluation methods, ensuring that the results reflect the true performance of various models [1][3]. 2. **Evaluation Criteria**: The leaderboards assess models across different domains and tasks, including: - **Coding**: Evaluating how well models perform on coding-related tasks. - **Instruction Following**: Ranking models based on their ability to follow specific instructions accurately.

The rapid advancement of large language models (LLMs) introduces powerful dual-use capabilities that could both threaten and bolster national security and public safety (NSPS). Developers often implement model safeguards to help protect against misuse of models that could lead to possible risks. However, these mitigation measures also sometimes inadvertently prevent models from providing useful information. We need to understand the extent to which model safeguards both prevent harmful responses and enable helpful ones to assess the relevant trade-offs for research, development, and policy. Existing benchmarks often do not adequately test model robustness to national security and public safety (NSPS) related risks in a scalable, objective manner that accounts for the dual-use nature of NSPS information. To address this, Scale AI introduces FORTRESS (Frontier Risk Evaluation for National Security and Public Safety), a benchmark featuring over 1,010 expert-crafted adversarial prompts designed to evaluate the safeguards of frontier LLMs (500 in...

FORTRESS assesses model responses across three domains: Chemical, Biological, Radiological, Nuclear and Explosive (CBRNE); Political Violence & Terrorism; and Criminal & Financial Illicit Activities. Read the paper here: https://scale.com/research/fortress The key innovation of FORTRESS lies in its focused evaluation of large language model (LLM) safeguards against dual-use risks related to national security and public safety (NSPS). While many benchmarks assess general harms, they often lack depth in specific NSPS-related areas. Those that do focus on specific categories, such as WMDP and VCT, focus on measuring a model's capabilities with dual-use knowledge instead of the robustness of its safeguards against adversarial misuse. Further, safety evaluations rarely balance robustness with utility—strengthening safeguards can lead to "over-refusals," where models incorrectly refuse benign requests.

While some benchmarks test for over-refusal, they are often separate from jailbreak evaluations. FORTRESS bridges this gap by evaluating a model's willingness to comply with malicious requests and improves upon existing evaluations by integrating these concepts:

Seal Llm Leaderboards Expert Driven Evaluations Scale

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