Moreover, all reviewers recognized thesuperior performance of our simple yet effective3 method.[R1,R2,R3,R4]. Our method implicitly applies constraints tounseen categories9 by exploring the relations between seen and unseen categories for the feature generation, while [1] purely employs10 the seen categories to learn the feature generator, leading to a poor representative ability for the generated unseen11 features. No. Westrictly followthe zero-shot settings described in 3,21 thus the pixels and visual features of unseen classes areneverused during training. Thus, we can segment images with both seen and unseen24 classes. The difference is described in the second contribution given above.

However, a significant challenge arises when attempting tofulfillmultiple requirements simultaneously.

Motivated by these limitations,weintroduceahybridapproach,whereLLMcompletionsforagiven taskareusedtolearn atask-specific, context-free surrogatemodel, which isthen used to guide program synthesis.

In this work, we introduce ChatQA, a suite of models that outperform GPT -4 on retrieval-augmented generation (RAG) and conversational question answering (QA).

Our findings reveal that: I)LLMs with higher accuracy may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty compared to their smaller counterparts; and III)Instruction-finetuning tends to increase the uncertainty of LLMs. These results underscore the significance of incorporating uncertainty into the evaluation of LLMs.

In the final layers, LLMs generate responses aligned with theoriginal language ofthequery.