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. |
| Approach: | They investigate linguistic alignment in a longitudinal dataset of real-world tutoring interactions and associated student test scores. |
| Outcome: | The proposed method can be applied to real-world tutoring interactions and student test scores. |
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| Challenge: | Large language models (LLMs) can answer prompts in many languages despite being pre-trained mostly on English text. |
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Pedagogical Alignment of Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are often used without pedagogical fine-tuning and provide immediate answers rather than guiding students through the problem-solving process. |
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Not that much power: Linguistic alignment is influenced more by low-level linguistic features rather than social power (P18-1)
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| 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. |
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From Language to Cognition: How LLMs Outgrow the Human Language Network (2025.emnlp-main)
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Badr AlKhamissi, Greta Tuckute, Yingtian Tang, Taha Osama A Binhuraib, Antoine Bosselut, Martin Schrimpf
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
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| Challenge: | Large language models are becoming more popular and are proving to be reliable . however, their reliability is often understudied due to their uncertainty and complex structure . |
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| Challenge: | masked language models produce stronger correlations than auto-regressive models, but humans and models make different response selection mistakes. |
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Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspective (2024.findings-emnlp)
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| Challenge: | a cognitive science research focus on aligning language spaces in their entirety . but, cognitive science has long focused on a local perspective . a new method for cross-lingual lexical alignment requires some methodology . |
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