LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers

TL;DR

The paper addresses the need to systematically generate suggestive limitations for research papers to aid peer review. It introduces Suggestive Limitation Generation (SLG) and the LimGen dataset of 4068 ACL papers with author-written limitations, and evaluates approaches ranging from summarization models to chain-modeling with Dense Passage Retrieval. The core contribution is a three-scheme benchmark (Non-truncated, DPR, Chain Modeling) showing that chain modeling with full-text input and distillation (Llama2-FT-Distilled) yields the strongest, more coherent limitations, while lexical metrics alone are insufficient. The work highlights practical implications for review workflows and identifies future directions, including multimodal content integration and improved evaluation methods.

Abstract

Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids readers in considering broader implications for further research. In this article, we present a novel and challenging task of Suggestive Limitation Generation (SLG) for research papers. We compile a dataset called \textbf{\textit{LimGen}}, encompassing 4068 research papers and their associated limitations from the ACL anthology. We investigate several approaches to harness large language models (LLMs) for producing suggestive limitations, by thoroughly examining the related challenges, practical insights, and potential opportunities. Our LimGen dataset and code can be accessed at \url{https://github.com/arbmf/LimGen}.

Figures (3)

• Figure 1: General architecture diagram for the suggestive limitation generation.
• Figure 2: Architecture diagram for DPR fine-tuning.
• Figure 3: Architecture diagram for Chain modeling.