While LLMs are trained on data originating from human sources (like books, research papers, websites, etc.), the key distinction is that the LLM is not simply copying or recalling exact information...

Question 1 of 2 for ChatGPT-4o: please read the paper “Can LLMs Generate Novel Research Ideas?” and tell me what it says in easy to understand language.

ChatGPT-4o’s answer:
The paper titled "Can LLMs Generate Novel Research Ideas?" explores whether large language models (LLMs) can generate research ideas that are as creative and innovative as those produced by human experts. The study involved over 100 experts in natural language processing (NLP) who generated ideas and reviewed both human and LLM-generated ideas in a blind comparison. The researchers conducted an extensive study with three types of ideas: those generated by humans, by LLMs, and by LLMs with human reranking to improve quality.

The key findings of the study include:

1. LLM-generated ideas were more novel: When reviewed by experts, LLM-generated ideas were consistently rated as more novel than human-generated ideas. This indicates that LLMs can suggest fresh and unique ideas that humans may not consider.

2. LLM ideas scored lower on feasibility: Although LLM-generated ideas were more novel, they were judged to be slightly less feasible compared to human ideas. This suggests that while LLMs can generate creative ideas, they may not always be practical or realistic to implement.

3. AI with human reranking improved quality: When LLM-generated ideas were reranked by humans, they not only retained their novelty but also showed better scores on overall quality and excitement, making the collaboration between humans and AI more effective.

4. Challenges for LLMs: Despite their potential, LLMs still face challenges, such as a lack of diversity in idea generation, over-reliance on certain methods, and difficulties in self-evaluation, meaning that LLMs are not yet ready to fully replace human researchers in generating practical research ideas.

The study highlights both the promise and the current limitations of LLMs in the creative aspects of scientific research, suggesting that human-AI collaboration can be beneficial for idea generation, but more work is needed to improve the practicality and diversity of AI-generated research ideas.


Question 2 of 2 for ChatGPT-4o: What is meant by "LLM-generated ideas"? Aren't all ideas generated by LLMs effectively originating from humans?

ChatGPT-4o’s answer:
"LLM-generated ideas" refer to research ideas that are produced directly by a large language model (LLM) like GPT-4 or similar systems. In the context of this study, it means that the researchers input prompts or instructions into the LLM, which then generates new ideas or suggestions based on the patterns it has learned from vast amounts of text data.

While it’s true that the LLMs are trained on data originating from human sources (like books, research papers, websites, etc.), the key distinction is that the LLM is not simply copying or recalling exact information from its training data. Instead, it combines, synthesizes, and reworks this information to generate new ideas that may not have been explicitly stated by humans before. In this sense, the ideas are "generated" by the LLM based on patterns in the data, but the underlying knowledge comes from human-created sources.

However, the point you're raising—that the ideas are ultimately grounded in human knowledge—is valid. The LLM does not have original creativity or consciousness. It works by predicting and assembling responses that seem appropriate based on the data it's seen. So, while the LLM can generate "novel" ideas by combining existing knowledge in new ways, it doesn't create ideas from nothing—it builds on what humans have already contributed to the vast dataset it was trained on.