Many recent prompting strategies for large language models (LLMs) query the
model multiple times sequentially -- first to produce intermediate results and
then the final answer. However, using these methods, both decoder and model are
unaware of potential follow-up prompts, leading to disconnected and undesirably
wordy intermediate responses. In this work, we address this issue by proposing
prompt sketching, a new prompting paradigm in which an LLM does not only
respond by completing a prompt, but by predicting values for multiple variables
in a template. This way, sketching grants users more control over the
generation process, e.g., by providing a reasoning framework via intermediate
instructions, leading to better overall results. The key idea enabling
sketching with existing, autoregressive models is to adapt the decoding
procedure to also score follow-up instructions during text generation, thus
optimizing overall template likelihood in inference. Our experiments show that
in a zero-shot setting, prompt sketching outperforms existing, sequential
prompting schemes such as direct asking or chain-of-thought on 7 out of 8 LLM
benchmarking tasks, including state tracking, arithmetic reasoning, and general
question answering. To facilitate future use, we release a number of generic,
yet effective sketches applicable to many tasks, and an open source library
called dclib, powering our sketch-aware decoders.

在这项工作中，我们通过提出提示素描的新提示范式来解决使用序列查询大型语言模型 (LLMs) 的许多最近提示策略中存在的问题，该模型不仅通过完成提示来回答，而是通过在模板中预测多个变量的值来回答，从而使用户在生成过程中具有更多的控制，同时优化推理中整体模板的可能性。

大型语言模型的提示速写

Prompt Sketching for Large Language Models

Automatic Speech Recognition (ASR) systems frequently use a search-based
decoding strategy aiming to find the best attainable transcript by considering
multiple candidates. One prominent speech recognition decoding heuristic is
beam search, which seeks the transcript with the greatest likelihood computed
using the predicted distribution. While showing substantial performance gains
in various tasks, beam search loses some of its effectiveness when the
predicted probabilities are highly confident, i.e., the predicted distribution
is massed for a single or very few classes. We show that recently proposed
Self-Supervised Learning (SSL)-based ASR models tend to yield exceptionally
confident predictions that may hamper beam search from truly considering a
diverse set of candidates. We perform a layer analysis to reveal and visualize
how predictions evolve, and propose a decoding procedure that improves the
performance of fine-tuned ASR models. Our proposed approach does not require
further training beyond the original fine-tuning, nor additional model
parameters. In fact, we find that our proposed method requires significantly
less inference computation than current approaches. We propose aggregating the
top M layers, potentially leveraging useful information encoded in intermediate
layers, and relaxing model confidence. We demonstrate the effectiveness of our
approach by conducting an empirical study on varying amounts of labeled
resources and different model sizes, showing consistent improvements in
particular when applied to low-resource scenarios.

该论文提出了一种解码过程，改进了现有的基于 beam search 的自动语音识别系统中的缺陷，特别是针对最近提出的自监督学习（SSL）模型中高度自信的预测所带来的问题，并且不需要额外的训练以及模型参数，并且在低资源场景中实现了一致的改进。