Modern language models, while sophisticated, exhibit some inherent
shortcomings, particularly in conversational settings. We claim that many of
the observed shortcomings can be attributed to violation of one or more
conversational principles. By drawing upon extensive research from both the
social science and AI communities, we propose a set of maxims -- quantity,
quality, relevance, manner, benevolence, and transparency -- for describing
effective human-AI conversation. We first justify the applicability of the
first four maxims (from Grice) in the context of human-AI interactions. We then
argue that two new maxims, benevolence (concerning the generation of, and
engagement with, harmful content) and transparency (concerning recognition of
one's knowledge boundaries, operational constraints, and intents), are
necessary for addressing behavior unique to modern human-AI interactions. The
proposed maxims offer prescriptive guidance on how to assess conversational
quality between humans and LLM-driven conversational agents, informing both
their evaluation and improved design.

通过借鉴社会科学和人工智能界的广泛研究，我们提出了一套最大量、最大质量、最大相关度、最大方式、最大善意和最大透明度的法则，旨在描述有效的人工智能对话，并为评估和改进设计提供了规范指导。

对话中的语言模型：人工智能与人类互动的交流法则

Language Models in Dialogue: Conversational Maxims for Human-AI  Interactions

Automatic Speech Recognition (ASR) in conversational settings presents unique
challenges, including extracting relevant contextual information from previous
conversational turns. Due to irrelevant content, error propagation, and
redundancy, existing methods struggle to extract longer and more effective
contexts. To address this issue, we introduce a novel Conversational ASR
system, extending the Conformer encoder-decoder model with cross-modal
conversational representation. Our approach leverages a cross-modal extractor
that combines pre-trained speech and text models through a specialized encoder
and a modal-level mask input. This enables the extraction of richer historical
speech context without explicit error propagation. We also incorporate
conditional latent variational modules to learn conversational level attributes
such as role preference and topic coherence. By introducing both cross-modal
and conversational representations into the decoder, our model retains context
over longer sentences without information loss, achieving relative accuracy
improvements of 8.8% and 23% on Mandarin conversation datasets HKUST and
MagicData-RAMC, respectively, compared to the standard Conformer model.

通过引入跨模态对话表示，结合预训练的语音和文本模型，扩展 Conformer 编码器 - 解码器模型，我们的方法能够提取更丰富的历史语音上下文，实现较标准 Conformer 模型相对准确度提升 8.8%（HKUST）和 23%（MagicData-RAMC）的结果。