LLM能否產生新穎的研究思路？與100多名自然語言處理研究人員進行的大規模人類研究

Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers

September 6, 2024

作者: Chenglei Si, Diyi Yang, Tatsunori Hashimoto

cs.AI

摘要

近期大型語言模型（LLMs）的進步引發了對其加速科學發現潛力的樂觀情緒，越來越多的研究提出研究代理人，可以自主生成和驗證新想法。儘管如此，目前尚未有評估顯示LLM系統能夠邁出第一步，即產生新穎且專家級的想法，更不用說執行整個研究過程。我們通過建立一個實驗設計，評估研究想法生成的同時控制混雜因素，並在專家自然語言處理研究人員和LLM構思代理人之間進行首次直接比較。通過招募100多名自然語言處理研究人員撰寫新想法，以及對LLM和人類想法進行盲檢閱，我們得出了有關當前LLM能力進行研究構思的第一個具統計學意義的結論：我們發現LLM生成的想法在新穎性上被評為更高（p < 0.05）而在可行性上則稍微弱於人類專家想法。通過仔細研究我們的代理人基準，我們確認了在構建和評估研究代理人時存在的問題，包括LLM自我評估的失敗以及它們在生成上缺乏多樣性。最後，我們承認人類對新穎性的判斷可能會很困難，即使是專家也一樣，並提出了一種端對端的研究設計，招募研究人員將這些想法實施為完整項目，使我們能夠研究這些新穎性和可行性判斷是否導致研究結果上的實質差異。

English

Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome.