arXiv AI

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?

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

arXiv AI

Jun 2

REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment

arXiv:2602. 14065v2 Announce Type: replace Abstract: Knowledge-intensive Visual Question Answering (KI-VQA) frequently suffers from severe knowledge conflicts caused by the inherent limitations of open-domain retrieval.

By Kai Ye, Xianwei Mao, Sheng Zhou, Zirui Shao, Ye Mo, Liangliang Liu, Haikuan Huang, Bin Li, Jiajun Bu

nlp fine-tuning safety

arXiv AI

Jul 21

Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG

arXiv:2607. 17742v1 Announce Type: new Abstract: Tables are a critical knowledge source in retrieval-augmented generation (RAG), but a retrieved table may lack sufficient evidence to answer a query, a property we call answerability.

By Jiaming Tian, Liyao Li, Wentao Ye, Haobo Wang, Lihua Yu, Zujie Ren, Gang Chen, Junbo Zhao

rag benchmarks

arXiv AI

Jun 9

DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking

arXiv:2602. 00238v2 Announce Type: replace-cross Abstract: Existing retrieval-augmented generation (RAG) systems often assume that each query has a single correct answer.

By Tianyi Hu, Niket Tandon, Akhil Arora

llms rag agents nlp safety

arXiv AI

Jun 6

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.

By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a

llms rag agents nlp fine-tuning benchmarks safety

arXiv AI

Jul 29

Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs

arXiv:2607. 25959v1 Announce Type: cross Abstract: Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation.

By Fanfu Wei, Thibault Ehrhart, Rapha\"el Troncy

llms rag multimodal benchmarks

arXiv AI

6d ago

CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement

arXiv:2608. 11631v1 Announce Type: new Abstract: In open-domain human-computer interaction scenarios, large language models (LLMs) frequently encounter user queries that are ambiguous or incomplete.

By Kuangzhao Yang, Ziliang Zhao, Zhicheng Dou

llms reinforcement-learning fine-tuning safety

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