With the remarkable development of large language models (LLMs), ensuring the
factuality of output has become a challenge. However, having all the contents
of the response with given knowledge or facts is not necessarily a good thing
in dialogues. This study aimed to achieve both attractiveness and factuality in
a dialogue response for which a task was set to predict sentences that do not
require factual correctness judgment such as agreeing, or personal
opinions/feelings. We created a dataset, dialogue dataset annotated with
fact-check-needed label (DDFC), for this task via crowdsourcing, and
classification tasks were performed on several models using this dataset. The
model with the highest classification accuracy could yield about 88% accurate
classification results.

大型语言模型在对话中实现吸引力和真实性的研究，通过众包创建了一个对话数据集，用于分类任务，并且最高分类准确率达到了 88%。

检测不需要事实判断的响应生成

Detecting Response Generation Not Requiring Factual Judgment

This paper explores the potential of constructing an AI spoken dialogue
system that "thinks how to respond" and "thinks how to speak" simultaneously,
which more closely aligns with the human speech production process compared to
the current cascade pipeline of independent chatbot and Text-to-Speech (TTS)
modules. We hypothesize that Large Language Models (LLMs) with billions of
parameters possess significant speech understanding capabilities and can
jointly model dialogue responses and linguistic features. We conduct two sets
of experiments: 1) Prosodic structure prediction, a typical front-end task in
TTS, demonstrating the speech understanding ability of LLMs, and 2) Further
integrating dialogue response and a wide array of linguistic features using a
unified encoding format. Our results indicate that the LLM-based approach is a
promising direction for building unified spoken dialogue systems.

本文研究构建一种 “思考如何回应” 和 “思考如何讲话” 的人工智能口语对话系统，相比当前独立的聊天机器人和文本到语音（TTS）模块的级联流水线，更贴近人类语音生成过程。研究使用拥有数十亿参数的大型语言模型（LLMs），展示其在语音理解能力上的潜力，并通过统一的编码格式进一步整合对话回应和各种语言特征。实验结果表明，基于 LLM 的方法是构建统一口语对话系统的一种有前景的方向。

基于大型语言模型的对话响应与语音合成的联合建模研究

Towards Joint Modeling of Dialogue Response and Speech Synthesis based  on Large Language Model

To diversify and enrich generated dialogue responses, knowledge-grounded
dialogue has been investigated in recent years. The existing methods tackle the
knowledge grounding challenge by retrieving the relevant sentences over a large
corpus and augmenting the dialogues with explicit extra information. Despite
their success, however, the existing works have drawbacks in inference
efficiency. This paper proposes KnowExpert, a framework to bypass the explicit
retrieval process and inject knowledge into the pre-trained language models
with lightweight adapters and adapt to the knowledge-grounded dialogue task. To
the best of our knowledge, this is the first attempt to tackle this challenge
without retrieval in this task under an open-domain chit-chat scenario. The
experimental results show that Knowexpert performs comparably with some
retrieval-based baselines while being time-efficient in inference,
demonstrating the effectiveness of our proposed method.

该论文介绍了一种名为 KnowExpert 的框架，将轻量级适配器注入预训练的语言模型中，在没有显式检索的情况下适应知识驱动的对话任务。实验结果表明，Knowexpert 的推理效率高，且在开放领域闲聊场景中的表现与一些基于检索的基线方法相当，证明了我们提出的方法的有效性。