75 citations · 326 across the 20 of their papers we have counts for
7 papers · 1 filter
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
Ryan Liu, Theodore R. Sumers, Ishita Dasgupta +1
In day-to-day communication, people often approximate the truth - for example, rounding the time or omitting details - in order to be maximally helpful to the listener. How do larg…
Machine Psychology
Thilo Hagendorff, Ishita Dasgupta, Marcel Binz +5
Large language models (LLMs) show increasingly advanced emergent capabilities and are being incorporated across various societal domains. Understanding their behavior and reasoning…
Transformers generalize differently from information stored in context vs in weights
Stephanie C. Y. Chan, Ishita Dasgupta, Junkyung Kim +3
Transformer models can use two fundamentally different kinds of information: information stored in weights during training, and information provided ``in-context'' at inference tim…
Language models show human-like content effects on reasoning tasks
Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan +5
Reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks, but exhibit many imperfections. Howe…
Can language models learn from explanations in context?
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan +6
Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We the…
Analyzing machine-learned representations: A natural language case study
Ishita Dasgupta, Demi Guo, Samuel J. Gershman +1
As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises of how the representations and decision rules they le…
Evaluating Compositionality in Sentence Embeddings
Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller +2
An important challenge for human-like AI is compositional semantics. Recent research has attempted to address this by using deep neural networks to learn vector space embeddings of…