1 citations · 1 across the 9 of their papers we have counts for
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Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake +1
Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is verifiable. Yet, many real-world r…
Identifying and Mitigating the Influence of the Prior Distribution in Large Language Models
Liyi Zhang, Veniamin Veselovsky, R. Thomas McCoy +1
Large language models (LLMs) sometimes fail to respond appropriately to deterministic tasks -- such as counting or forming acronyms -- because the implicit prior distribution they…
Levels of Analysis for Large Language Models
Alexander Y. Ku, Declan Campbell, Xuechunzi Bai +10
Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to…
Deep de Finetti: Recovering Topic Distributions from Large Language Models
Liyi Zhang, R. Thomas McCoy, Theodore R. Sumers +2
Large language models (LLMs) can produce long, coherent passages of text, suggesting that LLMs, although trained on next-word prediction, must represent the latent structure that c…