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Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers
Abstract Large Language Models (LLMs) have the capacity to store and recall facts. Through
experimentation with open-source models, we observe that this ability to retrieve facts can …
experimentation with open-source models, we observe that this ability to retrieve facts can …
Chaos with keywords: exposing large language models sycophancy to misleading keywords and evaluating defense strategies
This study explores the sycophantic tendencies of Large Language Models (LLMs), where
these models tend to provide answers that match what users want to hear, even if they are …
these models tend to provide answers that match what users want to hear, even if they are …
Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategies
This study explores the sycophantic tendencies of Large Language Models (LLMs), where
these models tend to provide answers that match what users want to hear, even if they are …
these models tend to provide answers that match what users want to hear, even if they are …
Large Language Models are In-context Teachers for Knowledge Reasoning
In this work, we study in-context teaching (ICT), where a teacher provides in-context
example rationales to teach a student to reason over unseen cases. Human teachers are …
example rationales to teach a student to reason over unseen cases. Human teachers are …
Associative memory inspires improvements for in-context learning using a novel attention residual stream architecture
Large language models (LLMs) demonstrate an impressive ability to utilise information
within the context of their input sequences to appropriately respond to data unseen by the …
within the context of their input sequences to appropriately respond to data unseen by the …