Papers by James A. Michaelov
Language Statistics and False Belief Reasoning: Evidence from 41 Open-Weight LMs (2026.acl-long)
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| Challenge: | a recent study on mental state reasoning in language models relies on a relatively small sample of closed-source LMs. |
| Approach: | They replicate and extend published work on false belief task by assessing LM mental state reasoning behavior across 41 open-weight models. |
| Outcome: | The results show that large LMs show higher sensitivity and predictive power . they also show that humans and LM models show a bias towards attributing false beliefs . |
On the Acquisition of Shared Grammatical Representations in Bilingual Language Models (2025.acl-long)
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| Challenge: | Crosslingual transfer is crucial to contemporary language models’ multilingual capabilities, but how it occurs is not well understood. |
| Approach: | They use structural priming to study grammatical representations in humans by controlling for training data quantity and language exposure. |
| Outcome: | The proposed model is able to learn a language in two languages and has a higher likelihood of learning a prepositional object (PO) dative sentence than a double object (DO) . |
Not quite Sherlock Holmes: Language model predictions do not reliably differentiate impossible from improbable events (2025.findings-acl)
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| Challenge: | Existing work has shown that language models can select the most likely or plausible of a set of possible events, but they are far from robust. |
| Approach: | They focus on whether language models can select the most likely or plausible of a set of possibilities and compare them to a broader behavior that humans exhibit largely unconsciously. |
| Outcome: | The proposed models perform worse than expected under certain conditions, compared with Llama 3, Gemma 2, and Mistral NeMo, and they are significantly more sensible than leaves. |
Do Language Models Make Human-like Predictions about the Coreferents of Italian Anaphoric Zero Pronouns? (2022.coling-1)
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| Challenge: | Some languages allow arguments to be omitted in certain contexts, but human language comprehenders construct expectations about which referents are more likely. |
| Approach: | They ask whether Neural Language Models extract expectations from sentences with zero pronouns from five behavioral experiments conducted in italian by Carminati (2005). |
| Outcome: | The results suggest that human expectations about coreference can be derived from exposure to language, and also indicates features of language models that allow them to better reflect human behavior. |