AI intent alignment, ensuring that AI produces outcomes as intended by users,
is a critical challenge in human-AI interaction. The emergence of generative
AI, including LLMs, has intensified the significance of this problem, as
interactions increasingly involve users specifying desired results for AI
systems. In order to support better AI intent alignment, we aim to explore
human strategies for intent specification in human-human communication. By
studying and comparing human-human and human-LLM communication, we identify key
strategies that can be applied to the design of AI systems that are more
effective at understanding and aligning with user intent. This study aims to
advance toward a human-centered AI system by bringing together human
communication strategies for the design of AI systems.

探索人类沟通策略以设计更加能理解和与用户意图对齐的人工智能系统，以实现人类中心的人工智能系统。

超越提示：从人类交流中学习，提升人工智能意图对齐

Beyond Prompts: Learning from Human Communication for Enhanced AI Intent  Alignment

Human-human communication is like a delicate dance where listeners and
speakers concurrently interact to maintain conversational dynamics. Hence, an
effective model for generating listener nonverbal behaviors requires
understanding the dyadic context and interaction. In this paper, we present an
effective framework for creating 3D facial motions in dyadic interactions.
Existing work consider a listener as a reactive agent with reflexive behaviors
to the speaker's voice and facial motions. The heart of our framework is Dyadic
Interaction Modeling (DIM), a pre-training approach that jointly models
speakers' and listeners' motions through masking and contrastive learning to
learn representations that capture the dyadic context. To enable the generation
of non-deterministic behaviors, we encode both listener and speaker motions
into discrete latent representations, through VQ-VAE. The pre-trained model is
further fine-tuned for motion generation. Extensive experiments demonstrate the
superiority of our framework in generating listener motions, establishing a new
state-of-the-art according to the quantitative measures capturing the diversity
and realism of generated motions. Qualitative results demonstrate the superior
capabilities of the proposed approach in generating diverse and realistic
expressions, eye blinks and head gestures.

人际交流模型可通过 Dyadic Interaction Modeling 生成 3D 面部动作，该模型考虑听者的非语言行为，利用编码离散潜在表示生成多样且逼真的运动。

社会行为生成的二人互动建模

Dyadic Interaction Modeling for Social Behavior Generation

In this paper, we discuss the potential of applying unsupervised anomaly
detection in constructing AI-based interactive systems that deal with highly
contextual situations, i.e., human-human communication, in collaboration with
domain experts. We reached this approach of utilizing unsupervised anomaly
detection through our experience of developing a computational support tool for
executive coaching, which taught us the importance of providing interpretable
results so that expert coaches can take both the results and contexts into
account. The key idea behind this approach is to leave room for expert coaches
to unleash their open-ended interpretations, rather than simplifying the nature
of social interactions to well-defined problems that are tractable by
conventional supervised algorithms. In addition, we found that this approach
can be extended to nurturing novice coaches; by prompting them to interpret the
results from the system, it can provide the coaches with educational
opportunities. Although the applicability of this approach should be validated
in other domains, we believe that the idea of leveraging unsupervised anomaly
detection to construct AI-based interactive systems would shed light on another
direction of human-AI communication.

讨论了利用非监督异常检测构建基于人工智能的交互式系统的潜力，重点是针对高度情境化的人人通信环境，可以与领域专家协作。