As large language models continue to develop in the field of AI, text generation systems are
susceptible to a worrisome phenomenon known as hallucination. In this study, we …

Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a
property of unitary agents devoid of social context. Given the success of contemporary …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

With the increasing ability of large language models (LLMs), in-context learning (ICL) has
become a new paradigm for natural language processing (NLP), where LLMs make …

Recent work claims that large language models display\textit {emergent abilities}, abilities
not present in smaller-scale models that are present in larger-scale models. What makes …

At present, the mechanisms of in-context learning in Transformers are not well understood
and remain mostly an intuition. In this paper, we suggest that training Transformers on auto …

The recent advent of large language models has reinvigorated debate over whether human
cognitive capacities might emerge in such generic models given sufficient training data. Of …

Multimodal datasets are a critical component in recent breakthroughs such as CLIP, Stable
Diffusion and GPT-4, yet their design does not receive the same research attention as model …

In-context learning is the ability of a model to condition on a prompt sequence consisting of
in-context examples (input-output pairs corresponding to some task) along with a new query …

We study how in-context learning (ICL) in language models is affected by semantic priors
versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with …