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Expert Disagreement Annotation: Capturing Genuine Ambiguity in High-Stakes LLM Training Data

Expert Disagreement Annotation: Capturing Genuine Ambiguity in High-Stakes LLM Training Data

LLMs are increasingly being implemented in high-stakes fields, such as medicine, law, finance, and government, where errors or overconfidence can have serious consequences. The reliability of such systems depends not only on the architecture of the models and training methods, but also on the quality of the data used

Training LLMs with Multi-Step Planning Data

Training LLMs with Multi-Step Planning Data

Analogical Reasoning Annotation: Training LLMs to Transfer Knowledge Across Domains

Analogical Reasoning Annotation: Training LLMs to Transfer Knowledge Across Domains

Causal Reasoning Annotation for LLMs: Labeling Cause-Effect Chains Beyond Simple Correlation

Causal Reasoning Annotation for LLMs: Labeling Cause-Effect Chains Beyond Simple Correlation

Why Human Annotation Still Matters for ADAS

Why Human Annotation Still Matters for ADAS

Policy Distillation Data for Robotics: Annotating Teacher-Student Pairs for Compact Deployable Models

Policy Distillation Data for Robotics: Annotating Teacher-Student Pairs for Compact Deployable Models

Human-Robot Object Handover Annotation: Labeling Timing, Grip Release, and Gaze Cues

Human-Robot Object Handover Annotation: Labeling Timing, Grip Release, and Gaze Cues

Data annotation

Expert Disagreement Annotation: Capturing Genuine Ambiguity in High-Stakes LLM Training Data

Expert Disagreement Annotation: Capturing Genuine Ambiguity in High-Stakes LLM Training Data

LLMs are increasingly being implemented in high-stakes fields, such as medicine, law, finance, and government, where errors or overconfidence can have serious consequences. The reliability of such systems depends not only on the architecture of the models and training methods, but also on the quality of the data used

Training LLMs with Multi-Step Planning Data

Training LLMs with Multi-Step Planning Data

Analogical Reasoning Annotation: Training LLMs to Transfer Knowledge Across Domains

Analogical Reasoning Annotation: Training LLMs to Transfer Knowledge Across Domains

Causal Reasoning Annotation for LLMs: Labeling Cause-Effect Chains Beyond Simple Correlation

Causal Reasoning Annotation for LLMs: Labeling Cause-Effect Chains Beyond Simple Correlation

Why Human Annotation Still Matters for ADAS

Why Human Annotation Still Matters for ADAS

Policy Distillation Data for Robotics: Annotating Teacher-Student Pairs for Compact Deployable Models

Policy Distillation Data for Robotics: Annotating Teacher-Student Pairs for Compact Deployable Models

Human-Robot Object Handover Annotation: Labeling Timing, Grip Release, and Gaze Cues

Human-Robot Object Handover Annotation: Labeling Timing, Grip Release, and Gaze Cues

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Validating HD Map Localization Accuracy for AVs

Validating HD Map Localization Accuracy for AVs

Around the development of autonomous transport, there is a widespread opinion that a self-driving car navigates in space exclusively by reading the road situation with cameras and sensors in real-time. In reality, the safe movement of robocars is almost impossible without the use of HD maps, which play

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