91 citations · 165 across the 33 of their papers we have counts for
8 papers · 1 filter
Models Can and Should Embrace the Communicative Nature of Human-Generated Math
Sasha Boguraev, Ben Lipkin, Leonie Weissweiler +1
Math is constructed by people for people: just as natural language corpora reflect not just propositions but the communicative goals of language users, the math data that models ar…
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez +7
Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs). But for what kinds of tasks is this extra ``thin…
Do they mean 'us'? Interpreting Referring Expressions in Intergroup Bias
Venkata S Govindarajan, Matianyu Zang, Kyle Mahowald +2
The variations between in-group and out-group speech (intergroup bias) are subtle and could underlie many social phenomena like stereotype perpetuation and implicit bias. In this p…
Participle-Prepended Nominals Have Lower Entropy Than Nominals Appended After the Participle
Kristie Denlinger, Stephen Wechsler, Kyle Mahowald
English allows for both compounds (e.g., London-made) and phrasal paraphrases (e.g., made in London). While these constructions have roughly the same truth-conditional meaning, we…
Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs
Kanishka Misra, Kyle Mahowald
Language models learn rare syntactic phenomena, but the extent to which this is attributable to generalization vs. memorization is a major open question. To that end, we iterativel…
Language models align with human judgments on key grammatical constructions
Jennifer Hu, Kyle Mahowald, Gary Lupyan +2
Do large language models (LLMs) make human-like linguistic generalizations? Dentella et al. (2023) ("DGL") prompt several LLMs ("Is the following sentence grammatically correct in…
Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently
Kanishka Misra, Allyson Ettinger, Kyle Mahowald
Recent zero-shot evaluations have highlighted important limitations in the abilities of language models (LMs) to perform meaning extraction. However, it is now well known that LMs…
Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs
Harvey Lederman, Kyle Mahowald
Are LLMs cultural technologies like photocopiers or printing presses, which transmit information but cannot create new content? A challenge for this idea, which we call bibliotechn…