EvalAssist is an open-source project that simplifies using large language models as evaluators (LLM-as-a-Judge) of the output of other large language models by supporting users in iteratively refin...

EvalAssist simplifies LLM-as-a-Judge by supporting users in iteratively refining evaluation criteria in a web-based user experience.

Evaluating AI model outputs at scale is a major challenge for teams using LLMs, especially when assessing nuanced qualities like politeness, fairness, and tone that traditional benchmarks miss. IBM Re...

by Osnat Mokryn (University of Haifa, IL), Orit Shaer (Wellesley College, US), Werner Geyer (IBM Research, US), Mary Lou Maher (Computing…

by Kush Varshney (IBM Research, US)

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IBM’s collection of LLM guardrail models take six of the top 10 spots on the new GuardBench leaderboard.

Caution: Experience required. Vibe coding feels like magic, until your AI assistant starts overwriting your work.

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IBM’s new Granite 3.1 addresses key enterprise needs, including expanded context handling, multilingual support, new tools and AI agent development

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YouTube video by Fahd Mirza

Although AI systems are increasingly being leveraged to provide value to organizations, individuals, and society, significant attendant risks have been identified and have manifested. These risks have...

While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advancements have been mad...

Pluralistic Alignment

LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty of LLM-as-a-Judge ev...

Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes time given the large a...