Advancing AI Systems
Robust, Trustwory Solutions

As leading AI researchers, Miniml push the boundaries of Machine Learning, NLP, and Generative AI. Our work advances the state of AI, focusing on mitigating hallucinations, enhancing model accuracy, and ensuring data security across diverse industries.

Using Natural Language Explanations
to Improve Robustness of In-context Learning

AI models are improving in decision-making, but their reliability against adversarial inputs remains a challenge. Miniml‘s research shows how integrating human-like natural language explanations can strengthen model robustness, delivering more accurate results across complex tasks.

Advancing AI Systems
Robust, Trustwory Solutions

Using Natural Language Explanations
to Improve Robustness of In-context Learning

Selected Publications

Inverse Scaling in Test-Time Compute

SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages

Adaptive Computation Modules: Granular Conditional Computation for Efficient Inference

Self-Training Large Language Models for Tool-Use Without Demonstrations

When Can Proxies Improve the Sample Complexity of Preference Learning?

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Advancing AI Systems Robust, Trustwory Solutions

Using Natural Language Explanations to Improve Robustness of In-context Learning

Selected Publications

Inverse Scaling in Test-Time Compute

SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages

Adaptive Computation Modules: Granular Conditional Computation for Efficient Inference

Self-Training Large Language Models for Tool-Use Without Demonstrations

When Can Proxies Improve the Sample Complexity of Preference Learning?

Get the latest industry insights delivered to you

Advancing AI Systems
Robust, Trustwory Solutions

Using Natural Language Explanations
to Improve Robustness of In-context Learning