LLMs Can Learn After Training?
Result discussed in here come from the paper "LARGER LANGUAGE MODELS DO IN-CONTEXT LEARNING DIFFERENTLY" from Google Research.
📌 https://arxiv.org/pdf/2303.03846
Really cool paper in general.
Abstract:
"We study how in-context learning (ICL) in language models is affected by semantic
priors versus input–label mappings. We investigate two setups—ICL with flipped
labels and ICL with semantically-unrelated labels—across various model families
(GPT-3, InstructGPT, Codex, PaLM, and Flan-PaLM).
First, experiments on ICL with flipped labels show that overriding semantic priors is an emergent ability of model scale. While small language models ignore flipped labels presented
in-context and thus rely primarily on semantic priors from pretraining, large models can override semantic priors when presented with in-context exemplars that contradict priors, despite the stronger semantic priors that larger models may hold.
We next study semantically-unrelated label ICL (SUL-ICL), in which labels are
semantically unrelated to their inputs (e.g., foo/bar instead of negative/positive),
thereby forcing language models to learn the input–label mappings shown in in-
context exemplars in order to perform the task.
The ability to do SUL-ICL also emerges primarily with scale, and large-enough language models can even perform linear classification in a SUL-ICL setting. Finally, we evaluate instruction-tuned
models and find that instruction tuning strengthens both the use of semantic priors and the capacity to learn input–label mappings, but more of the former."
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