Beyond Factual Correctness: Mitigating Preference-Inconsistent Explanations in Explainable Recommendation
Chengkai Wang, Baisong Liu
备注：
The authors have identified an issue in the evaluation protocol in Section 5.1.3. Feature extraction and semantic matching used to compute P-EHR require correction and re-validation, as they may not have been applied consistently across all generated explanations and baselines. This may affect part of the reported quantitative results and analysis, so the authors withdraw this version
LLM-based explainable recommenders can produce fluent explanations that are factually correct, yet still justify items using attributes that conflict with a user's historical preferences. Such preference-inconsistent explanations yield logically valid but unconvincing reasoning and are largely missed by standard hallucination or faithfulness metrics. We formalize this failure mode and propose PURE, a preference-aware reasoning framework following a select-then-generate paradigm. Instead of only improving generation, PURE intervenes in evidence selection, it selects a compact set of multi-hop item-centric reasoning paths that are both factually grounded and aligned with user preference structure, guided by user intent, specificity, and diversity to suppress generic, weakly personalized evidence. The selected evidence is then injected into LLM generation via structure-aware prompting that preserves relational constraints. To measure preference inconsistency, we introduce a feature-level, user-centric evaluation metric that reveals misalignment overlooked by factuality-based measures. Experiments on three real-world datasets show that PURE consistently reduces preference-inconsistent explanations and factual hallucinations while maintaining competitive recommendation accuracy, explanation quality, and inference efficiency. These results highlight that trustworthy explanations require not only factual correctness but also justification aligned with user preferences.
Amazon Web Services（亚马逊云服务）；Columbia University（哥伦比亚大学）
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The fact that automatic summarization may produce plausible-sounding yet inaccurate summaries is a major concern that limits its wide application. In this paper we present an approach to address factual consistency in summarization. We first propose an efficient automatic evaluation metric to measure factual consistency; next, we propose a novel learning algorithm that maximizes the proposed metric during model training. …
An Explainable Emotion Alignment Framework for LLM-Empowered Agent in Metaverse Service Ecosystem
College of Intelligence and Computing（智能与计算学院）；Tianjin University（天津大学）；Tianjin Key Laboratory of Healhy Habitat and Smart Technology（天津健康人居环境与智能技术重点实验室）；Laboratory of Computation and Analytics of Complex Management Systems（复杂管理系统计算与分析实验室）；Faculty of Environment, Science and Economy（环境、科学与经济学院）
Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Merced（加州大学默塞德分校）；University of Science and Technology of China（中国科学技术大学）
University of California, San Diego（加州大学圣迭戈分校）；University of California, Irvine（加州大学欧文分校）
Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings. The generated reviews, expressing users' estimated opinions towards related products, are often viewed as natural language 'rationales' for the jointly predicted rating. However, previous studies found that existing models often generate repetitive, universally applicable, and generic explanations, resulting in uninformative rationales. Further, our analysis shows that previous models' generated content often contain factual hallucinations. These issues call for novel solutions that co…
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
College of AI, Tsinghua University（清华大学人工智能学院）；Department of Electronic Engineering, BNRist, Tsinghua University（清华大学电子工程系）；School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子信息学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
University of North Carolina, Charlotte（北卡罗来纳大学夏洛特分校）；Amazon Alexa AI（亚马逊Alexa人工智能部门）
Recently neural response generation models have leveraged large pre-trained transformer models and knowledge snippets to generate relevant and informative responses. However, this does not guarantee that generated responses are factually correct. In this paper, we examine factual correctness in knowledge-grounded neural response generation models. We present a human annotation setup to identify three different response types: responses that are factually consistent with respect to the input knowledge, responses that contain hallucinated knowledge, and non-verifiable chitchat style responses. W…
Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph
CeADAR - Ireland’s Centre for Applied AI, University College Dublin（爱尔兰应用人工智能中心CeADAR、都柏林大学学院）
University College Dublin（都柏林大学学院）；Insight Centre for Data Analytics（Insight数据分析中心）；VistaMilk SFI Research Centre（VistaMilk SFI研究中心）
Recently, a groundswell of research has identified the use of counterfactual explanations as a potentially significant solution to the Explainable AI (XAI) problem. It is argued that (a) technically, these counterfactual cases can be generated by permuting problem-features until a class change is found, (b) psychologically, they are much more causally informative than factual explanations, (c) legally, they are GDPR-compliant. However, there are issues around the finding of good counterfactuals using current techniques (e.g. sparsity and plausibility). We show that many commonly-used datasets …
paluno - The Ruhr Institute for Software Technology（帕鲁诺——鲁尔软件技术研究所）
Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than simple models, one major drawback is their lack of interpretability, which limits their adoption in practice. We thus see growing interest in explainable predictive business process monitoring, which aims to increase the interpretability of prediction models. Existing solutions focus on giving factual explanations.While factual explanations can be helpful, humans typically do not ask why a particular prediction was mad…
Informative Semi-Factuals for XAI: The Elaborated Explanations that People Prefer
School of Computer Science, University College Dublin, Dublin, Ireland（都柏林大学计算机科学学院）；Insight Research Ireland Centre for Data Analytics, Dublin, Ireland（爱尔兰洞察研究数据分析中心）
University College Dublin（都柏林大学）；Insight Centre for Data Analytics（洞察数据分析中心）；VistaMilk SFI Research Centre（VistaMilk SFI研究中心）
Recently, it has been proposed that fruitful synergies may exist between Deep Learning (DL) and Case Based Reasoning (CBR); that there are insights to be gained by applying CBR ideas to problems in DL (what could be called DeepCBR). In this paper, we report on a program of research that applies CBR solutions to the problem of Explainable AI (XAI) in the DL. We describe a series of twin-systems pairings of opaque DL models with transparent CBR models that allow the latter to explain the former using factual, counterfactual and semi-factual explanation strategies. This twinning shows that functi…
University of Cyprus（塞浦路斯大学）；Open University of Cyprus（塞浦路斯开放大学）
This paper presents Abduction and Argumentation as two principled forms for reasoning, and fleshes out the fundamental role that they can play within Machine Learning. It reviews the state-of-the-art work over the past few decades on the link of these two reasoning forms with machine learning work, and from this it elaborates on how the explanation-generating role of Abduction and Argumentation makes them naturally-fitting mechanisms for the development of Explainable Machine Learning and AI systems. Abduction contributes towards this goal by facilitating learning through the transformation, p…
University College Dublin（都柏林大学学院）；Insight Centre for Data Analytics（数据分析洞察中心）；VistaMilk SFI Research Centre（VistaMilk SFI研究中心）
There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet counterfactual explanations have become massively popular in eXplainable AI (XAI) due to their proposed computational psychological, and legal benefits. In contrast however, semifactuals, which are a similar way humans commonly explain their reasoning, have surprisingly received no attention. Most counterfactual methods address tabular rather than imag…
Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations
Indiana University School of Medicine（印第安纳大学医学院）；Indiana University Melvin and Bren Simon Comprehensive Cancer Center（印第安纳大学梅尔文与布伦·西蒙综合癌症中心）；University of Warwick（华威大学）；Emory University（埃默里大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Singapore General Hospital（新加坡中央医院）
A Verifier-Guided Explainable Reasoning Framework with Gold-Anchored QLoRA, Task-Aware Mixture-of-Experts, and Group-Relative RLVR
University of Information Technology (UIT)（信息科技大学）；Ho Chi Minh City University of Technology (HCMUT)（胡志明市理工大学）；Vietnam National University, Ho Chi Minh City（越南国家大学胡志明市分校）；University of Economics Ho Chi Minh City (UEH)（胡志明市经济大学）
MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs
UC Santa Cruz（加州大学圣克鲁兹分校）；University of British Columbia（不列颠哥伦比亚大学）；Nanyang Technological University（南洋理工大学）；Stanford University（斯坦福大学）；Weill Cornell Medicine（威尔康奈尔医学院）；NYU Langone Health（纽约大学兰贡医学中心）；Chungnam National University Sejong Hospital（忠南大学世宗医院）；Vector Institute（矢量研究院）；Pusan National University Hospital（釜山国立大学医院）；Massachusetts General Hospital（麻省总医院）
Empirical Characterization of Rationale Stability Under Controlled Perturbations for Explainable Pattern Recognition
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Department of Computer Science, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校计算机科学系）；Department of Mechanical, Aerospace, and Industrial Engineering, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校机械、航空航天与工业工程系）
