44 citations · 51 across the 5 of their papers we have counts for
3 papers · 1 filter
Towards Execution-Grounded Automated AI Research
Chenglei Si, Zitong Yang, Yejin Choi +3
Automated AI research holds great potential to accelerate scientific discovery. However, current LLMs often generate plausible-looking but ineffective ideas. Execution grounding ma…
The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas
Chenglei Si, Tatsunori Hashimoto, Diyi Yang
Large Language Models (LLMs) have shown promise in accelerating the scientific research pipeline. A key capability for this process is the ability to generate novel research ideas,…
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
Chenglei Si, Diyi Yang, Tatsunori Hashimoto
Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing researc…