Bridging the Gap Between Machine and Human Language
Researchers at Stony Brook University and Salesforce AI Research are discovering why professional writers often feel disappointed with Large Language Models (LLMs) and are proposing a manually polished model that can aid in aligning a machine’s language to our own.
The rise of large language models has heralded a new era in artificial intelligence writing assistants, revolutionizing how we write, communicate and express ideas. From assisting in scientific research and crafting persuasive arguments to sparking creativity in literary works, LLMs have shown great promise. Yet, despite their potential, key differences between LLM-generated and human-written text persist, raising questions about their effectiveness in producing high-quality, human-like writing.
Recent research has illuminated these challenges, revealing that while LLMs such as GPT, Claude and Llama excel at generating text, they often fall short in certain creative and nuanced writing domains. A comprehensive study led by Assistant Professor Tuhin Chakrabarty, Department of Computer Science at Stony Brook University, and aided by professional writers, provides insight into these shortcomings, offering a roadmap for improving the alignment between machine-generated content and human writing. The paper was nominated for Best Paper and Honorable Mention Awards at CHI 2025.
Tuhin said, “One significant issue we noticed is that LLM-generated text often suffers from a lack of originality and variety.”
The widespread use of LLMs in writing tasks contributes to what is referred to as “algorithmic monoculture,” where content becomes overly homogenized. This is why writing with LLMs tends to result in repetitive, clichéd expressions and a lack of rhetorical depth. The texts often fail to demonstrate the complex, layered narrative techniques that characterize human creativity, frequently resorting to “telling” instead of “showing.”
Read the full story by Ankita Nagpal on the AI Innovation Institute website.