Humans exhibit strong preferences in syntactic alternations based on availability effects, where more 'available' constituents are ordered earlier in a sentence. Here, I show that autoregressive LMs learn these preferences both from training data and from architectural considerations on memory. I demonstrate that constraining context window size in both LSTM RNNs and n-gram models induces a preference towards producing short constituents before long ones. In particular, this has implications for incremental models of human language production.

The architecture gave the language model the constituent length preferences

**Workshop Organizers:** ‎‎Najoung Kim, Yiding Hao, Jaap Jumelet, Hosein Mohebbi, Yonatan Belinkov and Arya D. McCarthy
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W14: BlackboxNLP 2023: The 6th Workshop on Analysing and Interpreting Neural Networks for NLP

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**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

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Neil Rathi

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The architecture gave the language model the constituent length preferences

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