Beneath the Surface: Investigating LLMs' Capabilities for Communicating with Subtext
arXiv:2604.05273v1 Announce Type: new Abstract: Human communication is fundamentally creative, and often makes use of subtext -- implied meaning that goes beyond the literal content of the text. Here, we systematically study whether language models can use subtext in communicative settings, and introduce four new evaluation suites to assess these capabilities. Our evaluation settings range from writing & interpreting allegories to playing multi-agent and multi-modal games inspired by the rules of board games like Dixit. We find that frontier models generally exhibit a strong bias towards overly literal, explicit communication, and thereby fail to account for nuanced constraints -- even the best performing models generate literal clues 60% of times in one of our environments -- Visual Allusions. However, we find that some models can sometimes make use of common ground with another party to help them communicate with subtext, achieving 30%-50% reduction in overly literal clues; but they
arXiv:2604.05273v1 Announce Type: new Abstract: Human communication is fundamentally creative, and often makes use of subtext -- implied meaning that goes beyond the literal content of the text. Here, we systematically study whether language models can use subtext in communicative settings, and introduce four new evaluation suites to assess these capabilities. Our evaluation settings range from writing & interpreting allegories to playing multi-agent and multi-modal games inspired by the rules of board games like Dixit. We find that frontier models generally exhibit a strong bias towards overly literal, explicit communication, and thereby fail to account for nuanced constraints -- even the best performing models generate literal clues 60% of times in one of our environments -- Visual Allusions. However, we find that some models can sometimes make use of common ground with another party to help them communicate with subtext, achieving 30%-50% reduction in overly literal clues; but they struggle at inferring presence of a common ground when not explicitly stated. For allegory understanding, we find paratextual and persona conditions to significantly shift the interpretation of subtext. Overall, our work provides quantifiable measures for an inherently complex and subjective phenomenon like subtext and reveals many weaknesses and idiosyncrasies of current LLMs. We hope this research to inspire future work towards socially grounded creative communication and reasoning.
Executive Summary
This study investigates the ability of large language models (LLMs) to engage in communicative subtext—implied meaning beyond literal content—a hallmark of human interaction. Through four novel evaluation suites, including allegory interpretation and multi-agent games, the research reveals a critical limitation: frontier LLMs demonstrate a strong bias toward explicit, literal communication, failing to leverage nuanced constraints. While some models partially succeed by exploiting common ground, their performance remains inconsistent, particularly in inferring unexpressed shared understanding. The work quantifies an inherently subjective phenomenon, offering measurable benchmarks that underscore the gap between current LLMs and human-like communicative subtlety. These findings highlight the need for socially grounded creative reasoning in AI, with broader implications for fields such as human-computer interaction and AI alignment.
Key Points
- ▸ LLMs exhibit a systemic bias toward literal communication, struggling with subtext even in tasks designed to elicit nuanced interaction (e.g., generating overly literal clues 60% of the time in Visual Allusions).
- ▸ Some LLMs can partially leverage common ground to enhance subtextual communication, achieving a 30%-50% reduction in literal clues in certain settings, though this capability is inconsistent and context-dependent.
- ▸ Interpretation of subtext is highly sensitive to paratextual and persona conditions in allegory tasks, suggesting that LLMs' subtextual reasoning is fragile and susceptible to framing effects.
Merits
Novel Evaluation Framework
The introduction of four distinct evaluation suites—ranging from allegory interpretation to multi-agent games—provides a rigorous, multidimensional framework for assessing subtextual communication, addressing a critical gap in AI evaluation methodologies.
Quantitative Rigor
By quantifying performance metrics (e.g., frequency of literal clues, reductions in literal communication), the study moves beyond qualitative assessments, enabling objective comparison and reproducibility in a traditionally subjective domain.
Interdisciplinary Insight
The work bridges computational linguistics, cognitive science, and AI ethics by examining how LLMs handle a core aspect of human communication—subtext—while highlighting the need for socially grounded AI systems.
Demerits
Limited Generalization
The evaluation suites, while innovative, are constrained to specific tasks (e.g., Dixit-inspired games, allegory interpretation) and may not fully capture the complexity of subtext in real-world, unstructured communication.
Overreliance on Synthetic Tasks
The study's reliance on constructed environments (e.g., multi-agent games with predefined rules) risks overlooking the organic, unpredictable nature of subtext in natural human interactions.
Inconsistent Model Performance
The variability in model performance—even among frontier LLMs—suggests that subtextual capabilities are not yet robust or scalable, limiting their practical applicability in dynamic social contexts.
Expert Commentary
This study is a timely and rigorous exploration of a critical yet underexplored dimension of LLM capabilities: subtextual communication. The authors’ introduction of diverse evaluation suites is particularly commendable, as it moves beyond traditional benchmarking to probe the pragmatic aspects of language—an area where LLMs have historically lagged. The findings reveal a troubling trend: even the most advanced models default to literalism, a tendency that could significantly hinder their utility in real-world applications requiring subtlety, such as negotiation or conflict resolution. The partial success of some models in leveraging common ground is intriguing and suggests that subtextual reasoning is not entirely absent but rather underdeveloped. This work also raises profound questions about the alignment between LLMs and human communication norms. If AI systems are to become true partners in human endeavors, they must not only process language but also understand its implicit layers. Future research should focus on integrating cognitive models of pragmatics into LLM architectures, perhaps drawing from speech act theory or relevance theory, to bridge this gap. The study’s limitations—such as reliance on synthetic tasks—do not detract from its value but rather highlight the need for more ecologically valid evaluations. In sum, this paper is a call to action for the AI community to prioritize the development of models that can engage in the rich, layered communication that defines human interaction.
Recommendations
- ✓ Expand evaluation suites to include more ecologically valid, real-world scenarios (e.g., workplace negotiations, therapeutic interactions) to assess subtextual capabilities in unstructured environments.
- ✓ Develop hybrid training approaches that combine symbolic reasoning (e.g., pragmatic inference rules) with neural architectures to enhance models' ability to infer and generate subtext.
- ✓ Collaborate with cognitive scientists and linguists to integrate theoretical models of pragmatics into LLM training, ensuring that models are not only statistically proficient but also cognitively plausible in their communication strategies.
- ✓ Establish interdisciplinary research consortia to study subtext across modalities, including visual, auditory, and haptic cues, to develop multimodal AI systems capable of holistic communication.
- ✓ Advocate for industry-wide standards and benchmarks for subtextual communication in LLMs, with input from ethicists, policymakers, and domain experts to ensure responsible deployment in sensitive domains.
Sources
Original: arXiv - cs.CL