Challenge: linguistic alignment between interlocutors of higher power is attributed to their relative social power, but studies on low-level linguistic features do not account for these factors.
Approach: They characterize the effect of power on alignment with logistic regression models in two datasets and find it vanishes after controlling for low-level features such as utterance length.
Outcome: The proposed model shows that the effect vanishes or is reversed after controlling for low-level features such as utterance length.

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Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations? (2026.acl-long)

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Challenge: Power differences shape human communication through well-documented socio-cognitive effects . asymmetric relationships or power differentials give rise to well-known socio-computational effects - lianelli, 1976 .
Approach: They simulate multi-turn, power-asymmetric dialogues with personas from diverse professions . they find that LLMs show key socio-cognitive effects of power, albeit with nuances and variability .
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Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer (2026.acl-srw)

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Challenge: Large language models (LLMs) have advanced natural language processing, yet their benefits remain concentrated in English and a small number of high-resource languages.
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)

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Challenge: Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models.
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Linguistic Alignment Predicts Learning in Small Group Tutoring Sessions (2025.findings-emnlp)

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Challenge: Cognitive science offers rich theories of learning and communication, yet these are often difficult to operationalize at scale.
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Tracing Linguistic Markers of Influence in a Large Online Organisation (2023.acl-short)

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Challenge: Social science and psycholinguistic research have shown that power and status affect how people use language in a range of domains.
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A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space (2021.emnlp-main)

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Challenge: Cross-lingual language models house representations for many different languages in the same space.
Approach: They investigate linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs.
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From Distributional to Overton Pluralism: Investigating Large Language Model Alignment (2025.naacl-long)

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Challenge: a large language model's (LLM) output distribution is changed by an alignment process . a recent study shows that aligned models surface information that cannot be recovered from base models without fine-tuning.
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LLMs syntactically adapt their language use to their conversational partner (2025.acl-short)

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Challenge: Adapting to the language of a communication partner is associated with increased success in goal-oriented conversations.
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Alignment, Acceptance, and Rejection of Group Identities in Online Political Discourse (N18-4)

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Challenge: linguistic alignment is a robust and robust form of communication accommodation, and has been detected in a variety of linguistic interactions, ranging from speed dates to the Supreme Court.
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Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs (2025.findings-emnlp)

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Challenge: Large language models can lead to undesired consequences when misaligned with human values . previous studies have shown misalignment of LLMs with human value using expert-designed or agent-based emulated bias scenarios .
Approach: They investigate whether large language models (LLMs) are misaligned with human values . they find no significant differences in understanding of HVSB between LLMs .
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