Safe deployment of large language models (LLMs) may benefit from a reliable
method for assessing their generated content to determine when to abstain or to
selectively generate. While likelihood-based metrics such as perplexity are
widely employed, recent research has demonstrated the limitations of using
sequence-level probability estimates given by LLMs as reliable indicators of
generation quality. Conversely, LLMs have demonstrated strong calibration at
the token level, particularly when it comes to choosing correct answers in
multiple-choice questions or evaluating true/false statements. In this work, we
reformulate open-ended generation tasks into token-level prediction tasks, and
leverage LLMs' superior calibration at the token level. We instruct an LLM to
self-evaluate its answers, employing either a multi-way comparison or a
point-wise evaluation approach, with the option to include a ``None of the
above'' option to express the model's uncertainty explicitly. We benchmark a
range of scoring methods based on self-evaluation and evaluate their
performance in selective generation using TruthfulQA and TL;DR. Through
experiments with PaLM-2 and GPT-3, we demonstrate that self-evaluation based
scores not only improve accuracy, but also correlate better with the overall
quality of generated content.

使用大型语言模型进行自我评估可以提高生成内容的准确性，并与生成内容的整体质量更好地相关。

大型语言模型中自我评估提高选择性生成

Self-Evaluation Improves Selective Generation in Large Language Models

Machine learning algorithms typically assume independent and identically
distributed samples in training and at test time. Much work has shown that
high-performing ML classifiers can degrade significantly and provide
overly-confident, wrong classification predictions, particularly for
out-of-distribution (OOD) inputs. Conditional language models (CLMs) are
predominantly trained to classify the next token in an output sequence, and may
suffer even worse degradation on OOD inputs as the prediction is done
auto-regressively over many steps. Furthermore, the space of potential
low-quality outputs is larger as arbitrary text can be generated and it is
important to know when to trust the generated output. We present a highly
accurate and lightweight OOD detection method for CLMs, and demonstrate its
effectiveness on abstractive summarization and translation. We also show how
our method can be used under the common and realistic setting of distribution
shift for selective generation (analogous to selective prediction for
classification) of high-quality outputs, while automatically abstaining from
low-quality ones, enabling safer deployment of generative language models.

本文介绍了一种高精度且轻量级的适用于条件语言模型的 OOD 检测方法，并展示了其在抽象摘要和翻译方面的有效性。在分布偏移的情况下，我们的方法可以被用于高质量输出的选择生成，同时自动避免低质量的输出，从而增加自然语言生成模型的安全性。

有条件语言模型的越界检测和选择性生成

Out-of-Distribution Detection and Selective Generation for Conditional Language Models

We propose an end-to-end, domain-independent neural encoder-aligner-decoder
model for selective generation, i.e., the joint task of content selection and
surface realization. Our model first encodes a full set of over-determined
database event records via an LSTM-based recurrent neural network, then
utilizes a novel coarse-to-fine aligner to identify the small subset of salient
records to talk about, and finally employs a decoder to generate free-form
descriptions of the aligned, selected records. Our model achieves the best
selection and generation results reported to-date (with 59% relative
improvement in generation) on the benchmark WeatherGov dataset, despite using
no specialized features or linguistic resources. Using an improved k-nearest
neighbor beam filter helps further. We also perform a series of ablations and
visualizations to elucidate the contributions of our key model components.
Lastly, we evaluate the generalizability of our model on the RoboCup dataset,
and get results that are competitive with or better than the state-of-the-art,
despite being severely data-starved.

该论文提出了一种端到端的，领域无关的神经编码器 - 对齐器 - 解码器模型，用于选择性生成，即内容选择和表面实现的联合任务。