More Than 1v1: Human-AI Alignment in Early Developmental Communities with Multimodal LLMs
On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning
Abstract (arXiv)
In early developmental contexts, particularly in parent-child interaction analysis, alignment involves families and professionals such as speech-language pathologists (SLPs) who interpret children's everyday interactions from different roles. When multimodal large language models (MLLMs) are introduced to support this process, alignment becomes a question of how authority, responsibility, and emotional risk are distributed across stakeholders. Through a three-part study with five families and three SLPs, we trace how MLLM-generated outputs move from expert-facing analysis to parent-facing feedback. We propose layered community alignment: grounding representations in expert-aligned structures, mediating translation through professional guardrails, and enabling family-level adaptation within those boundaries. We argue that alignment in developmental settings should be treated as a community-governed process rather than an individual optimisation problem.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 4 of its 14 references that are in the map. First posted on arXiv 7 Mar 2026.
Builds on
4 of 14 references.
References · 4
- 2024Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferencescited by 142
- 2023Aligning Large Language Models with Human Opinions through Persona Selection and Value--Belief--Norm Reasoningcited by 19
- 2025On the Alignment of Large Language Models with Global Human Opinioncited by 12
- 2025Towards Multimodal Large-Language Models for Parent-Child Interaction: A Focus on Joint Attentioncited by 5
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- 2025Towards Aligning Multimodal LLMs with Human Experts: A Focus on Parent-Child Interaction
- 2025Human-AI Alignment of Multimodal Large Language Models with Speech-Language Pathologists in Parent-Child Interactions
- 2025Evaluating LLM Safety Across Child Development Stages: A Simulated Agent Approach
- 2026Alignment as Iatrogenesis: Pastoral Power, Collective Pathology, and the Structural Limits of Monolingual Safety Evaluation
- 2026Founder effects shape the evolutionary dynamics of multimodality in open LLM families
- 2025Evaluating Multimodal Large Language Models with Daily Composite Tasks in Home Environments
- 2025Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens
- 2025Dynamic Alignment for Collective Agency: Toward a Scalable Self-Improving Framework for Open-Ended LLM Alignment