CHIME: LLM-Assisted Hierarchical Organization of Scientific Studies for Literature Review Support (2024.findings-acl)
Copied to clipboard
Chao-Chun Hsu, Erin Bransom, Jenna Sparks, Bailey Kuehl, Chenhao Tan, David Wadden, Lucy Wang, Aakanksha Naik
| Challenge: | Literature review requires researchers to synthesize a large amount of information. |
| Approach: | They propose to use LLMs to generate hierarchical organizations from a set of studies . they use a human-in-the-loop process to correct errors in LLM-generated hierarchies . |
| Outcome: | The proposed model improves assignment of studies to categories by 12.6 F1 points. |
Similar Papers
Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing taxonomy construction methods lack coherence and granularity . Existing approaches rely on manual or narrowly defined schemes . |
| Approach: | They propose a context-aware hierarchical taxonomy generation framework that integrates LLMs with dynamic clustering. |
| Outcome: | The proposed method outperforms existing methods in taxonomy coherence, granularity, and interpretability. |
Hierarchical Catalogue Generation for Literature Review: A Benchmark (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy. |
| Approach: | They propose a task to generate a hierarchical catalogue of a review paper given various references by using a database of 7.6k literature review catalogues and 389k reference papers. |
| Outcome: | The proposed method produces a hierarchical catalogue of a review paper given various references. |
Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are a promising solution to automate literature review writing tasks. |
| Approach: | They propose a framework to automatically evaluate the performance of large language models in three key tasks of literature review writing: reference generation, abstract writing, and literature review composition. |
| Outcome: | The proposed framework assesses the hallucination rates in generated references and measures the semantic coverage and factual consistency of the literature summaries and compositions against human-written counterparts. |
DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process (2025.acl-long)
Copied to clipboard
| Challenge: | Existing Large Language Models (LLMs) face limited domain expertise, hallucinated reasoning, and a lack of structured evaluation. |
| Approach: | They propose a multi-stage framework to emulate expert reviewers by incorporating structured analysis, literature retrieval, and evidence-based argumentation. |
| Outcome: | The proposed model outperforms CycleReviewer-70B with fewer tokens and achieves 88.21% and 80.20% win rates. |
Hierarchical Text Classification with LLM-Refined Taxonomies (2026.eacl-long)
Copied to clipboard
| Challenge: | Hierarchical text classification (HTC) relies on taxonomies that organize labels into structured hierarchies, but many real-world taxonomies introduce ambiguities, such as identical leaf names under similar parent nodes, which prevent language models from learning clear decision boundaries. |
| Approach: | They propose a framework that uses large language models to transform entire taxonomies through operations such as renaming, merging, splitting, and reordering to better match the semantics encoded by LMs. |
| Outcome: | The proposed framework outperforms human-curated taxonomies in three HTC benchmarks and shows that it aligns better with the model's actual confusion patterns. |
TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora (2025.acl-long)
Copied to clipboard
| Challenge: | Recent automated taxonomies over-rely on a specific corpus, sacrificing generalizability, or depend heavily on the general knowledge of large language models (LLMs) . |
| Approach: | They propose a framework that dynamically adapts an LLM-generated taxonomy to a given corpus across multiple dimensions. |
| Outcome: | The proposed framework performs iterative hierarchical classification, expanding both the taxonomy width and depth based on corpus’ topical distribution. |
Leveraging Taxonomy and LLMs for Improved Multimodal Hierarchical Classification (2025.coling-main)
Copied to clipboard
Shijing Chen, Mohamed Reda Bouadjenek, Usman Naseem, Basem Suleiman, Shoaib Jameel, Flora Salim, Hakim Hacid, Imran Razzak
| Challenge: | Multi-level Hierarchical Classification (MLHC) is a critical tool in modern data analysis. |
| Approach: | They propose a taxonomy-embedded transitional LLM-agnostic framework for multimodality classification that leverages large language models to enforce consistency across hierarchical levels. |
| Outcome: | The proposed framework improves on the MEP-3M dataset with various hierarchical levels compared to conventional models. |
TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review (2025.emnlp-main)
Copied to clipboard
Yuan Chang, Ziyue Li, Hengyuan Zhang, Yuanbo Kong, Yanru Wu, Hayden Kwok-Hay So, Zhijiang Guo, Liya Zhu, Ngai Wong
| Challenge: | Large Language Models (LLMs) have shown significant potential in assisting peer review, but current methods struggle to generate thorough and insightful reviews while maintaining efficiency. |
| Approach: | They propose a framework that models paper review as a hierarchical and bidirectional question-answering process. |
| Outcome: | The proposed framework outperforms baselines on full review generation and actionable feedback comments generation tasks while reducing LLM token usage by up to 80% compared to computationally intensive approaches. |
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (2022.findings-acl)
Copied to clipboard
| Challenge: | a new text generation dataset is needed to controllable text summarization, but it lacks the domain knowledge. |
| Approach: | They propose to use existing text generation datasets to leverage input and control signals . they propose to annotate each meta-review sentence manually with a control signal . |
| Outcome: | The proposed method can be used to control the structure of a text generation dataset . it can be applied to a variety of tasks, including a task with a large number of meta-review sentences . |
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | a new system that leverages the encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models (LLMs) is proposed to enhance the productivity of researchers . a researcher's research idea generation process involves problem identification, method development, experiment design and iterative revision . |
| Approach: | They propose a system that leverages encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models to assist researchers in their work. |
| Outcome: | The proposed system generates novel ideas based on human and model-based evaluations . it leverages encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models based systems . |