Papers by Sean Trott
Multimodal Language Models Show Evidence of Embodied Simulation (2024.lrec-main)
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| Challenge: | Multimodal large language models (MLLMs) are gaining popularity as partial solutions to the “symbol grounding problem” faced by language models trained on text alone. |
| Approach: | They propose to use multimodal large language models to integrate linguistic representations with data from other modalities to investigate whether they are integrated into a model. |
| Outcome: | The proposed models are sensitive to visual features like object shape when it is implied by a verbal description of an event. |
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 . |
RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English) (2021.acl-long)
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| Challenge: | lexical ambiguity is a problem for NLP, but few tasks evaluate its impact on human intuitions. |
| Approach: | They propose to use contextualized word embeddings to evaluate word meaning . they use a dataset of human relatedness judgments and human estimates of sense dominance . |
| Outcome: | The proposed model matches human intuitions with contextualized embeddings on 112 ambiguous words in context with 672 sentence pairs. |
Evaluating Contextualized Representations of (Spanish) Ambiguous Words: A New Lexical Resource and Empirical Analysis (2025.naacl-long)
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| Challenge: | Few studies have systematically compared LMs’ contextualized word embeddings for languages beyond English. |
| Approach: | They evaluate Spanish ambiguous nouns in context in a suite of Spanish-language monolingual and multilingual BERT-based models. |
| Outcome: | The proposed model captures some variance in human relatedness judgments but falls short of the human benchmark. |
(Re)construing Meaning in NLP (2020.acl-main)
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| Challenge: | a new paper explores the role of linguistic choices in interpreting information in natural language . linguistic choice is a way of expressing information, but it is not the meaning of an utterance, authors argue . |
| Approach: | They propose to define construal as a way of conceptualizing or construing information . they propose to use this concept to develop theoretical and practical work in NLP . |
| Outcome: | The proposed study explores how construal can inform theoretical and practical work in NLP. |
Seeing Through Words, Speaking Through Pixels: Deep Representational Alignment Between Vision and Language Models (2025.emnlp-main)
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| Challenge: | Recent studies show that deep vision-only and language-only models project inputs into a partially aligned representational space. |
| Approach: | They investigate whether a model's representational code is semantically shared . they find that alignment peaks in mid-to-late layers of both model types . |
| Outcome: | a forced-choice "Pick-a-Pic" task shows human preferences for image-caption matches are mirrored in embedding spaces across vision-language model pairs. |