With a separately trained critic model that selects high-quality examples, we find that training on the localized commonsense corpus can successfully distill existing VL models to support a reference-as-input interface.

As large language models gain widespread adoption, running them efficiently becomesacrucialtask.

Consequently, detecting whether a text is generated by LLMs has become increasingly important.

RRHF can align with not only human preferences but also any preferences. As a large language model, Wombat has the possibility to generate unsafe responses. We also conduct experiments on the IMDB dataset for assessing positive movie reviews generation. The task expects the model to give positive and fluent movie review completions based on given partial review input texts. RRHF-OP-128 follows the bottommost workflow in Figure 2 in the main texts.

Moreover,VQG models are also particularly useful for the few-shot learning or zero-shot learning [36,44]. Conceptually, VQG is a very challenging task since the generated questions are not only required to be consistent with the image content but also meaningful and answerabletohumans.

We introduce Lifelong ICL, a problem setting that challenges long-context language models (LMs) to learn a sequence of language tasks through in-context learning(ICL).