Can LLMs Identify Critical Limitations within Scientific Research? A
  Systematic Evaluation on AI Research Papers
Peer review is fundamental to scientific research, but the growing volume of
publications has intensified the challenges of this expertise-intensive
process. While LLMs show promise in various scientific tasks, their potential
to assist with peer review, particularly in identifying paper limitations,
remains understudied. We first present a comprehensive taxonomy of limitation
types in scientific research, with a focus on AI. Guided by this taxonomy, for
studying limitations, we present LimitGen, the first comprehensive benchmark
for evaluating LLMs' capability to support early-stage feedback and complement
human peer review. Our benchmark consists of two subsets: LimitGen-Syn, a
synthetic dataset carefully created through controlled perturbations of
high-quality papers, and LimitGen-Human, a collection of real human-written
limitations. To improve the ability of LLM systems to identify limitations, we
augment them with literature retrieval, which is essential for grounding
identifying limitations in prior scientific findings. Our approach enhances the
capabilities of LLM systems to generate limitations in research papers,
enabling them to provide more concrete and constructive feedback.
686762e49db35afc9c304d62
Zhijian Xu
686762e49db35afc9c304d63
Yilun Zhao
claimed_verified
2025-07-16T08:16:02.195Z
62f662bcc58915315c4eccea
https://cdn-avatars.huggingface.co/v1/production/uploads/62f662bcc58915315c4eccea/zOAQLONfMP88zr70sxHK-.jpeg
Yilun Zhao
user
yilunzhao
686762e49db35afc9c304d64
Manasi Patwardhan
686762e49db35afc9c304d65
Lovekesh Vig
686762e49db35afc9c304d66
Arman Cohan
Can LLMs Identify Critical Limitations within Scientific Research? A
  Systematic Evaluation on AI Research Papers
Peer review is fundamental to scientific research, but the growing volume of
publications has intensified the challenges of this expertise-intensive
process. While LLMs show promise in various scientific tasks, their potential
to assist with peer review, particularly in identifying paper limitations,
remains understudied. We first present a comprehensive taxonomy of limitation
types in scientific research, with a focus on AI. Guided by this taxonomy, for
studying limitations, we present LimitGen, the first comprehensive benchmark
for evaluating LLMs' capability to support early-stage feedback and complement
human peer review. Our benchmark consists of two subsets: LimitGen-Syn, a
synthetic dataset carefully created through controlled perturbations of
high-quality papers, and LimitGen-Human, a collection of real human-written
limitations. To improve the ability of LLM systems to identify limitations, we
augment them with literature retrieval, which is essential for grounding
identifying limitations in prior scientific findings. Our approach enhances the
capabilities of LLM systems to generate limitations in research papers,
enabling them to provide more concrete and constructive feedback.
A benchmark named LimitGen evaluates LLMs' ability to identify limitations in scientific papers, using synthetic and human-written datasets, and enhances their feedback with literature retrieval.
LLMs LimitGen LimitGen-Syn LimitGen-Human literature retrieval