Explain why answer generation and sampling methodologies create an evaluation asymmetry when comparing pre-trained base models to post-RLHF models on free-response tasks.
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Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
Ch.3 Model Alignment and Safety - Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
Impact of RLHF on Model Capability - Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
Related
When attempting to evaluate pre-trained base models against post-RLHF models on an equal footing, which format presents fewer evaluation difficulties than free-response tasks?
Raw pre-trained base models inherently possess the capacity to accurately follow open-ended instructions.
Which post-training processes develop a model's capacity to follow open-ended instructions accurately?
Explain why answer generation and sampling methodologies create an evaluation asymmetry when comparing pre-trained base models to post-RLHF models on free-response tasks.