Papers by Ethan Gotlieb Wilcox
Function Words as Statistical Cues for Language Learning (2026.acl-long)
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| Challenge: | Existing studies have argued that function words aid learning abstract grammatical knowledge from linear input. |
| Approach: | They examine the statistical distribution of function words and their properties . they show that function words are reliable, diverse, and informative . |
| Outcome: | The results show that function words preserve high frequency, reliable syntactic association, phrase-boundary alignment and are informative to structural dependency. |
What Can String Probability Tell Us About Grammaticality? (2026.tacl-1)
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| Challenge: | linguistic theories have argued that language models have largely achieved grammatical competence, but they will assign non-zero probability to all strings. |
| Approach: | They propose a theoretical framework for analyzing string probabilities in linguistics based on simple assumptions about the generative process of corpus data. |
| Outcome: | The proposed framework makes three predictions using 280K sentence pairs in English and Chinese. |
What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channels (2026.acl-long)
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| Challenge: | Prosody—the melody of speech—conveys critical information often not captured by the words or text of a message. |
| Approach: | They propose an information-theoretic approach to quantify how much is conveyed by prosody that is not recoverable from text alone. |
| Outcome: | The proposed framework can quantify how much is conveyed by prosody that is not recoverable from text alone and crucially, what prosody conveys. |
Dual Alignment Between Language Model Layers and Human Sentence Processing (2026.acl-long)
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| Challenge: | Existing studies have demonstrated both the successes and limitations of accurate predictability estimation by modern LMs in cognitive modeling. |
| Approach: | They propose to use internal layers to better estimate human cognitive effort observed in syntactic ambiguity processing in English. |
| Outcome: | The proposed models can be modeled using surprisal from early layers of large language models (LLMs) this raises the question whether such advantages extend to more syntactically challenging constructions, where surprised estimates underestimate human cognitive effort. |