33 citations · 83 across the 20 of their papers we have counts for
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If LLMs Have Human-Like Attributes, Then So Does Age of Empires II
Adrian de Wynter
Much research has been carried out on large language models (LLMs) and LLM-powered agentic workflows. However, many works within the field state emergence of, ascribe to, or assume…
The Hrunting of AI: Where and How to Improve English Dialectal Fairness
Wei Li, Adrian de Wynter
It is known that large language models (LLMs) underperform in English dialects, and that improving them is difficult due to data scarcity. In this work we investigate how quality a…
Causal Reasoning Favors Encoders: On The Limits of Decoder-Only Models
Amartya Roy, Elamparithy M, Kripabandhu Ghosh +2
In context learning (ICL) underpins recent advances in large language models (LLMs), although its role and performance in causal reasoning remains unclear. Causal reasoning demands…
Is In-Context Learning Learning?
Adrian de Wynter
In-context learning (ICL) allows some autoregressive models to solve tasks via next-token prediction and without needing further training. This has led to claims about these model'…
Evaluating Style-Personalized Text Generation: Challenges and Directions
Anubhav Jangra, Bahareh Sarrafzadeh, Silviu Cucerzan +2
With the surge of large language models (LLMs) and their ability to produce customized output, style-personalized text generation--"write like me"--has become a rapidly growing are…
The Thin Line Between Comprehension and Persuasion in LLMs
Adrian de Wynter, Tangming Yuan
Large language models (LLMs) are excellent at maintaining high-level, convincing dialogue, but it remains unclear whether their persuasive success reflects genuine understanding of…