Copertina

RWESummary: A Framework and Test for Choosing Large Language Models to Summarize Real-World Evidence (RWE) Studies

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Mukerji, Arjun, Jackson, Michael L., Jones, Jason, Sanghavi, Neil
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

An Empirical Study of Many-to-Many Summarization with Large Language Models
di: Wang, Jiaan, et al.
Pubblicazione: (2025)

Evaluating Large Language Models on Financial Report Summarization: An Empirical Study
di: Yang, Xinqi, et al.
Pubblicazione: (2024)

Exploring Iterative Controllable Summarization with Large Language Models
di: Ryu, Sangwon, et al.
Pubblicazione: (2024)

Evaluating Large Language Models for Real-World Engineering Tasks
di: Heesch, Rene, et al.
Pubblicazione: (2025)

Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities
di: Lehmann, Hans Hergen, et al.
Pubblicazione: (2025)

Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
di: Chrysostomou, George, et al.
Pubblicazione: (2023)

Cross-Lingual Conversational Speech Summarization with Large Language Models
di: Nelson, Max, et al.
Pubblicazione: (2024)

Can Large Language Models Understand Real-World Complex Instructions?
di: He, Qianyu, et al.
Pubblicazione: (2023)

Multimodal Large Language Models to Support Real-World Fact-Checking
di: Geng, Jiahui, et al.
Pubblicazione: (2024)

Aligning Large Language Models for Enhancing Psychiatric Interviews Through Symptom Delineation and Summarization: Pilot Study
di: So, Jae-hee, et al.
Pubblicazione: (2024)

FocusMed: A Large Language Model-based Framework for Enhancing Medical Question Summarization with Focus Identification
di: Liu, Chao, et al.
Pubblicazione: (2025)

Large, Small or Both: A Novel Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization
di: Zhang, Yanyue, et al.
Pubblicazione: (2024)

Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization
di: Deroy, Aniket, et al.
Pubblicazione: (2024)

LLaMandement: Large Language Models for Summarization of French Legislative Proposals
di: Gesnouin, Joseph, et al.
Pubblicazione: (2024)

Gene Set Summarization using Large Language Models
di: Joachimiak, Marcin P., et al.
Pubblicazione: (2023)

Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions
di: Luo, Man, et al.
Pubblicazione: (2024)

RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
di: Yang, Shuo, et al.
Pubblicazione: (2025)

Self-Cognition in Large Language Models: An Exploratory Study
di: Chen, Dongping, et al.
Pubblicazione: (2024)

Analyzing the Performance of Large Language Models on Code Summarization
di: Haldar, Rajarshi, et al.
Pubblicazione: (2024)

Helping Large Language Models Protect Themselves: An Enhanced Filtering and Summarization System
di: Muhaimin, Sheikh Samit, et al.
Pubblicazione: (2025)

Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?
di: Fonseca, Marcio, et al.
Pubblicazione: (2024)

CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels
di: Wei, Lingxiao, et al.
Pubblicazione: (2024)

Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models
di: Wang, Qingyue, et al.
Pubblicazione: (2023)

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
di: Luo, Ziyang, et al.
Pubblicazione: (2025)

Large Language Models for Code Summarization
di: Szalontai, Balázs, et al.
Pubblicazione: (2024)

Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data
di: Kwack, TaeYoon, et al.
Pubblicazione: (2025)

Evaluating Computational Accuracy of Large Language Models in Numerical Reasoning Tasks for Healthcare Applications
di: Malghan, Arjun R.
Pubblicazione: (2025)

Improving Automatic Summarization of Radiology Reports through Mid-Training of Large Language Models
di: Lyu, Mengxian, et al.
Pubblicazione: (2026)

IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization
di: Cao, Jie, et al.
Pubblicazione: (2024)

Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
di: Croxford, Emma, et al.
Pubblicazione: (2024)

Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency
di: Li, Taiji, et al.
Pubblicazione: (2024)

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques
di: Aly, Walid Mohamed, et al.
Pubblicazione: (2025)

A Framework for Real-time Safeguarding the Text Generation of Large Language Model
di: Dong, Ximing, et al.
Pubblicazione: (2024)

Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models
di: Han, Chen, et al.
Pubblicazione: (2025)

ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization
di: Bae, Suyoung, et al.
Pubblicazione: (2026)

Unraveling the Capabilities of Language Models in News Summarization
di: Odabaşı, Abdurrahman, et al.
Pubblicazione: (2025)

Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims
di: Ma, Fan, et al.
Pubblicazione: (2026)

RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
di: Jin, Zhuoran, et al.
Pubblicazione: (2024)

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search
di: Kim, Edward, et al.
Pubblicazione: (2024)

Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models
di: Zhang, Yanyue, et al.
Pubblicazione: (2025)