arXiv AI June 24, 2026 By Shuo Guan

Faithful by Construction: Claim-Anchored Attribution for Multi-Document Summarization

arXiv:2606. 23989v1 Announce Type: cross Abstract: End-to-end large language models (LLMs) produce fluent multi-document summaries but remain prone to hallucination, and the attributions they offer are typically coarse (whole documents or passages) and generated post hoc, leaving each summary statement hard to verify.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 5

Beyond Representational Similarity: Source-Conditioned Description-Length Gain for Generative Plagiarism Detection and Candidate Source Reranking

arXiv:2608. 03859v1 Announce Type: cross Abstract: Large language models (LLMs) pose challenges to academic integrity and peer review.

By Peijia Guo, Wenxuan Xie, ZiGuang Li, Ming Li

llms rag benchmarks

arXiv AI

Jun 26

ProvenAI: Provenance-Native Traces of Evidence in Generated Answers

arXiv:2606. 26449v1 Announce Type: cross Abstract: Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source meaningfully shaped the output.

By Mohammad Faizan, Dalal Alharthi

rag agents nlp benchmarks

arXiv AI

Jul 28

How Context Attribution Handles What the Model Already Knows

arXiv:2607. 23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response.

By Quoc-Huy Trinh, Lin Zhu, Sebastian Szyller

llms benchmarks

arXiv AI

Jun 26

ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence

arXiv:2606. 26437v1 Announce Type: cross Abstract: Existing metrics for factuality and faithfulness evaluate whether an answer is supported or contradicted by its grounding documents, but they fail to capture when both supporting and contradicting evidence coexist.

By Siyi Liu, Aaron Halfaker, Dan Roth, Patrick Xia

llms benchmarks

arXiv AI

Jul 24

Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers

arXiv:2607. 21010v1 Announce Type: new Abstract: Zero-shot summarization using Large Language Models (LLMs) has significantly advanced the abstractive summarization task by producing coherent and fluent summaries.

By Vasudha Bhatnagar, Purnima Bindal, Vikas Kumar, Raj Kumari Bahl

llms nlp benchmarks safety

arXiv AI

Jun 19

CREDENCE: Claim Reduction for Decomposition & Enhanced Credibility -- Semantic Metrics and Convergence Analysis

arXiv:2606. 19819v1 Announce Type: cross Abstract: Decomposing compound sentences into atomic, verifiable claims is a prerequisite for reliable automated fact-checking.

By Phuong Huu Vu Tran, Thuan Duc Mai, Bach Xuan Le

llms benchmarks

Related stories

Beyond Representational Similarity: Source-Conditioned Description-Length Gain for Generative Plagiarism Detection and Candidate Source Reranking

ProvenAI: Provenance-Native Traces of Evidence in Generated Answers

How Context Attribution Handles What the Model Already Knows

ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence

Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers

CREDENCE: Claim Reduction for Decomposition & Enhanced Credibility -- Semantic Metrics and Convergence Analysis