Cover Image

TrueBrief: Faithful Summarization through Small Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lakara, Kumud, Shi, Ruibo, Silavong, Fran
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Automatic Labelling with Open-source LLMs using Dynamic Label Schema Integration
by: Walshe, Thomas, et al.
Published: (2025)

Industry-Aligned Granular Topic Modeling
by: Moon, Sae Young, et al.
Published: (2026)

STORYSUMM: Evaluating Faithfulness in Story Summarization
by: Subbiah, Melanie, et al.
Published: (2024)

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

Faithful Chart Summarization with ChaTS-Pi
by: Krichene, Syrine, et al.
Published: (2024)

Evaluating Small Language Models for News Summarization: Implications and Factors Influencing Performance
by: Xu, Borui, et al.
Published: (2025)

FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
by: Bao, Forrest Sheng, et al.
Published: (2024)

AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization
by: Piya, Fahmida Liza, et al.
Published: (2026)

FaithUn: Toward Faithful Forgetting in Language Models by Investigating the Interconnectedness of Knowledge
by: Yang, Nakyeong, et al.
Published: (2025)

Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization
by: Mei, Xiaoyong, et al.
Published: (2026)

Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge
by: Wang, Mengyu, et al.
Published: (2026)

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

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

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

Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences
by: Ghoshal, Asish, et al.
Published: (2022)

FaithLM: Towards Faithful Explanations for Large Language Models
by: Chuang, Yu-Neng, et al.
Published: (2024)

Closing the Confidence-Faithfulness Gap in Large Language Models
by: Miao, Miranda Muqing, et al.
Published: (2026)

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

The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models
by: Siegel, Noah Y., et al.
Published: (2024)

Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
by: Luo, Linhao, et al.
Published: (2023)

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

SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
by: Yang, Yu, et al.
Published: (2024)

Small Language Model Can Self-correct
by: Han, Haixia, et al.
Published: (2024)

RSAT: Structured Attribution Makes Small Language Models Faithful Table Reasoners
by: Gajjar, Jugal, et al.
Published: (2026)

Breaking the Trade-Off Between Faithfulness and Expressiveness for Large Language Models
by: Yang, Chenxu, et al.
Published: (2025)

Fine-Tuned Language Models for Domain-Specific Summarization and Tagging
by: Wang, Jun, et al.
Published: (2025)

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

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

Don't Believe Everything You Read: Enhancing Summarization Interpretability through Automatic Identification of Hallucinations in Large Language Models
by: Vakharia, Priyesh, et al.
Published: (2023)

Can Small Language Models Handle Context-Summarized Multi-Turn Customer-Service QA? A Synthetic Data-Driven Comparative Evaluation
by: Cooray, Lakshan, et al.
Published: (2026)

Comparing Explanation Faithfulness between Multilingual and Monolingual Fine-tuned Language Models
by: Zhao, Zhixue, et al.
Published: (2024)

To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts
by: Huang, Yukun, et al.
Published: (2024)

FaithLens: Detecting and Explaining Faithfulness Hallucination
by: Si, Shuzheng, et al.
Published: (2025)

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

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

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

Faithfulness-QA: A Counterfactual Entity Substitution Dataset for Training Context-Faithful RAG Models
by: Ju, Li, et al.
Published: (2026)

Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models
by: Wei, Yifan, et al.
Published: (2024)

Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style
by: Li, Yuepei, et al.
Published: (2024)

FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"
by: Ming, Yifei, et al.
Published: (2024)