Modern large language models (LLMs) represent a paradigm shift in what can
plausibly be expected of machine learning models. The fact that LLMs can
effectively generate sensible answers to a diverse range of queries suggests
that they would be useful in customer support applications. While powerful,
LLMs have been observed to be prone to hallucination which unfortunately makes
their near term use in customer support applications challenging. To address
this issue we present a system that allows us to use an LLM to augment our
customer support advocates by re-framing the language modeling task as a
discriminative classification task. In this framing, we seek to present the
top-K best template responses for a customer support advocate to use when
responding to a customer. We present the result of both offline and online
experiments where we observed offline gains and statistically significant
online lifts for our experimental system. Along the way, we present observed
scaling curves for validation loss and top-K accuracy, resulted from model
parameter ablation studies. We close by discussing the space of trade-offs with
respect to model size, latency, and accuracy as well as and suggesting future
applications to explore.

现代大型语言模型开启了机器学习模型的一个范式转变。虽然这些模型能够对多样化的问题给出合理回答，但它们也容易出现虚构信息，这让它们在客户支持应用上的短期使用变得具有挑战性。为了解决这个问题，作者提出了一种系统，将语言模型任务转化为判别性分类任务，以增强客户支持的效果。通过离线和在线实验，我们观察到实验系统在离线和在线方面都取得了积极结果。此外，作者提供了模型参数剪枝所得的验证损失和 top-K 准确率的观察结果，以及在模型大小、延迟和准确度之间的权衡空间，并提出了未来可以探索的应用领域。

大型语言模型中的判别分类标度律

Scaling Laws for Discriminative Classification in Large Language Models

State of the art models in intent induction require annotated datasets.
However, annotating dialogues is time-consuming, laborious and expensive. In
this work, we propose a completely unsupervised framework for intent induction
within a dialogue. In addition, we show how pre-processing the dialogue corpora
can improve results. Finally, we show how to extract the dialogue flows of
intentions by investigating the most common sequences. Although we test our
work in the MultiWOZ dataset, the fact that this framework requires no prior
knowledge make it applicable to any possible use case, making it very relevant
to real world customer support applications across industry.

我们提出了一个完全无监督的对话意图识别框架，旨在通过研究最常见的对话序列来提取意图流，并且在处理对话语料库时进行预处理以提高结果。此框架不需要先验知识，适用于任何可能的用例，非常适用于各行业的实际客户支持应用。