33 citations · 127 across the 31 of their papers we have counts for
5 papers · 1 filter
What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs
Nhi Nguyen, Shauli Ravfogel, Rajesh Ranganath
Large language models (LLMs) are increasingly deployed in high-stakes domains, where free-text explanations such as chain-of-thought and post-hoc rationales are used to justify mod…
Geometric Factual Recall in Transformers
Shauli Ravfogel, Gilad Yehudai, Joan Bruna +1
How do transformer language models memorize factual associations? A common view casts internal weight matrices as associative memories over pairs of embeddings, requiring parameter…
Can LLMs Introspect? A Reality Check
Shashwat Singh, Tal Linzen, Shauli Ravfogel
Can large language models detect and report their own internal states? A number of recent studies have argued that they can. Drawing on lessons from human metacognition research, w…
The Truthfulness Spectrum Hypothesis
Zhuofan Josh Ying, Shauli Ravfogel, Nikolaus Kriegeskorte +1
Large language models (LLMs) have been reported to linearly encode truthfulness, yet recent work questions this finding's generality. We reconcile these views with the truthfulness…
Discrete Diffusion Models Exploit Asymmetry to Solve Lookahead Planning Tasks
Itamar Trainin, Shauli Ravfogel, Omri Abend +1
While Autoregressive (AR) Transformer-based Generative Language Models are frequently employed for lookahead tasks, recent research suggests a potential discrepancy in their abilit…