arXiv AI August 5, 2026 By Jiwon Lee, Yong-chan Park, Jungin Hong, U Kang

Diversity is Not Ambiguity: Toward Accurate and Efficient Ambiguity Detection for Open-Domain QA

arXiv:2608. 03177v1 Announce Type: new Abstract: How can question answering (QA) systems determine whether a query is ambiguous?

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

arXiv AI

Jun 4

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

arXiv:2606. 04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text.

By Mengao Zhang, Xiang Yang, Chang Liu, Tianhui Tan, Ke-wei Huang

llms rag nlp benchmarks data-engineering

arXiv AI

Jun 9

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

arXiv:2605. 17301v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice.

By Chenyu Wang, Yueyuan Li, Yingmin Liu, Yang Shu

llms rag benchmarks

arXiv AI

Jun 2

The Role of Ambiguity in Error Prediction via Uncertainty Quantification

arXiv:2606. 02093v1 Announce Type: cross Abstract: The task of Error Prediction, namely predicting whether a model output is correct, is commonly tackled with Uncertainty Quantification (UQ).

By Ieva Raminta Stali\=unait\.e, James Bishop, Andreas Vlachos

llms nlp

arXiv AI

Jun 6

Improving Answer Extraction in Context-based Question Answering Systems Using LLMs

arXiv:2606. 06197v1 Announce Type: cross Abstract: Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs).

By Hafez Abdelghaffar, Ahmed Alansary, Ali Hamdi

llms nlp fine-tuning benchmarks

arXiv AI

Jun 29

Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering

arXiv:2606. 28076v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts.

By Yongxue Shan, Meihan Wu, Cundi Fang, Jie Peng, Xiaodong Wang

nlp

arXiv AI

Jul 21

Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification

arXiv:2607. 16868v1 Announce Type: new Abstract: Large Language Models (LLMs) often produce confidently stated yet unreliable outputs, posing critical challenges for deployment in safety-sensitive applications.

By Yanni Dong, Minghua Liu, Meiling Zhu, Xiaowei Huang, Lijun Zhang

llms benchmarks safety

Related stories

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

The Role of Ambiguity in Error Prediction via Uncertainty Quantification

Improving Answer Extraction in Context-based Question Answering Systems Using LLMs

Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering

Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification