Implicit vs. Explicit Prompting Strategies for LVLMs in Referential Communication

2026-06-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

This study investigates the effectiveness of implicit versus explicit prompting strategies for Large Vision-Language Models (LVLMs) in referential communication, addressing contradictory findings from Jones et al. (2026) and Zeng et al. (2026). By controlling for task differences, the research replicates the observation that LVLMs can achieve efficient referring expressions when explicitly prompted. However, the same models consistently fail to infer the necessity for communicative efficiency from more implicit prompts. This highlights a fundamental divergence between human and AI communication patterns, suggesting that LVLMs require direct instructions to optimize for efficiency in referential tasks, unlike humans who might infer such needs. The findings underscore the importance of prompt design for effective LVLM coordination.

Key takeaway

For prompt engineers developing LVLM applications requiring precise and efficient communication, you should prioritize explicit instructions for communicative goals. Your models will likely fail to infer the need for efficiency from implicit cues, leading to suboptimal performance in referential tasks. Therefore, ensure your prompts directly specify desired communication characteristics, such as brevity or clarity, to achieve reliable coordination and avoid misinterpretations that could arise from human-like implicit assumptions.

Key insights

LVLMs require explicit prompts for efficient referential communication, unlike humans.

Principles

• Explicit prompts enable LVLM communicative efficiency.
• LVLMs struggle with implicit communicative inference.
• Human and AI communication differ fundamentally.

Method

The study directly compared prompting styles (explicit vs. implicit) for LVLMs in referential communication tasks, controlling for other task differences between prior contradictory studies.

In practice

• Design prompts with direct efficiency instructions.
• Test LVLM performance across prompt explicitness.

Topics

• Large Vision-Language Models
• Prompt Engineering
• Referential Communication
• Explicit Prompting
• Implicit Prompting
• AI Communication
• Natural Language Processing

Best for: AI Engineer, Machine Learning Engineer, Research Scientist, AI Scientist, NLP Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.