The paper introduces TextQ‑German, a dataset suite for evaluating German natural language generation (NLG) from a Quality of Experience (QoE) perspective, covering tasks such as summarization and machine translation. Human ratings collected via crowdsourcing identify perceptual quality dimensions, and the authors develop automatic QoE prediction models—including transformer‑based, linguistic feature‑based, and hybrid approaches—showing that hybrid models outperform pure transformers and that linguistic features alone can rival fine‑tuned language models. The dataset is further enriched with large language model (LLM) outputs annotated with overall QoE scores, and validation on held‑out data demonstrates generalization to unseen data.
"whyItMatters":"The resource provides a publicly accessible benchmark and baseline models for human‑centered NLG evaluation, enabling the development of systems that better align with human quality perception."
arXiv:2507.01594v2 Announce Type: replace
Abstract: Task-oriented dialogue (ToD) systems aim to help users accomplish goals through natural language interaction. Beyond task success, effective ToD sy...
By Shutong Feng, Hsien-chin Lin, Nurul Lubis, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Renato Vukovic, Milica Ga\v{s}i\'c
UXBench is the first user‑centric benchmark that uses real user feedback to evaluate AI assistants. It comprises three tasks—UX Judge, UX Eval, and UX Recovery—built from 7,400 test instances drawn from over 70,000 interaction logs of a mainstream Chinese AI assistant, covering 8 scenarios and 83 domains. Experiments on 26 state‑of‑the‑art language models reveal how model capability affects user experience and highlight six key findings about user feedback prediction and engagement.
By Mengze Hong, Xia Zeng, Zeyang Lei, Sheng Wang, Chen Jason Zhang, Di Jiang, Taiming Fu, Jinfeng Huang, Mengqiao Liu, Qinghe Chang, Haosheng Zou, Qiongyi Zhou, Sijun He, Simonjmdeng, Minlong Peng, Di Liang
arXiv:2608. 13606v1 Announce Type: new Abstract: The next generation of AI agents is increasingly moving beyond systems that answer isolated questions toward persistent personal assistants that can understand, remember, and continuously learn from users' experiences.
By Xinle Deng, Yida Xue, Xiangyuan Ru, Haoming Xu, Shuofei Qiao, Mengru Wang, Yijun Chen, Buqiang Xu, Chen Jiang, Yuchen Eleanor Jiang, Lizhong Wang, Jianfeng Wang, Li Zeng, Haofen Wang, Guilin Qi, Huajun Chen, Ningyu Zhang
arXiv:2606. 06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data.
By Lechen Zhang, Jiarui Liu, Tal August
Scalaris is a scalable enterprise memory architecture designed to balance retrieval accuracy and context efficiency for conversational AI assistants. It separates user interactions and assistant responses into distinct long‑term and short‑term memory streams, each with specialized representations and ingestion/retrieval pipelines. The system achieves state‑of‑the‑art benchmark scores while reducing retrieved context tokens by about tenfold and improving overall response quality by 36 % in proprietary deployments.
By Devarsh Gandhi, Sarthak Pawar, Hemprasad Badgujar, Vaibhav Rathore, Siddharth Sureka, Neha Bhargava
UXBench Pro is a new benchmark that evaluates personalized user experience in multi-turn dialogue interactions. It contains 1,000 test instances from real user interactions across 12 task scenarios and 82 domains, each paired with a FACTORS user profile describing seven behavioral facets. The benchmark introduces a dual-perspective evaluation: a personalized User Reward Model for third‑person judgment and Sim4Eval, a user simulator for first‑person evaluation across four cognitive state dimensions, along with meta‑benchmarks URMBench and USimBench to assess evaluator reliability.
By Mengze Hong, Zeyang Lei, Wenbo Shang, Xia Zeng, Xiying Zhao, Qi Zhu, Chen Jason Zhang, Di Jiang, Taiming Fu, Qiongyi Zhou, Qinghe Chang, Fubao Zhang, Chenxuan Ma, Minlong Peng, Jinfeng Huang, Zineng Zhou, Jindou Wu, Muge Qi, Sijun He, Xin Cui, Di Liang, Yuan Hua, Davey Chen
The paper introduces TRACER, a multi‑turn user simulator that models evolving user intent and aligns simulated behavior with real interaction trajectories. TRACER is trained first with supervised fine‑tuning on real dialogues and then with reinforcement learning that uses hierarchical outcome‑ and trajectory‑level rewards to address reward sparsity and credit assignment. In real customer‑service sessions, TRACER‑7B outperforms the best baseline by 11.4 conversion F1, achieves the lowest group‑level conversion‑rate error and semantic trajectory distance, and generalizes to out‑of‑distribution scenarios, while human Turing tests show its conversations appear natural. The authors also present the Dynamic Marketing Benchmark, which evaluates both persuasion effectiveness and response quality of large language models through simulated interactions, demonstrating that higher response quality does not always lead to higher conversion rates.
By Geng Chen, Ruotong Pan, Zhirui Yang, Qiqi He, Jiawei Chen, Zhang Yunfei, Chongyuan Chen, Minxuan Lv, Zheng Yang, Win-Bin Huang, Xiangyu Wu, Wenwu Ou
arXiv:2609.17010v1 Announce Type: new
Abstract: Lifelong conversational agents rely on memory systems to maintain deep, context-aware interactions with users. However, existing explicit textual memor...
By Cai Ke, Xin Liu, Han Zhang, Jiangyue Yan, Zike Yuan, Ling Deng, Yue Yu, Hui Wang, Ruifeng Xu
arXiv:2601. 06862v2 Announce Type: replace-cross Abstract: The quality of the user experience has become one of the most important aspects in todays world, as it directly influences individuals willingness to continue using or abandon a product or service.
By Michael Sidorov, Ofer Hadar
arXiv:2604. 25834v2 Announce Type: replace Abstract: With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms.
By Wenhao Li, Zihan Lin, Zhengxiao Guo, Jie Zhou, Shukai Liu, Yongqi Liu, Chuan Luo, Chaoyi Ma, Ruiming Tang, Han Li
arXiv:2606. 24042v1 Announce Type: new Abstract: Recommender systems often induce filter bubbles and semantic homogenization by monolithically optimizing for immediate user engagement.
By Cl\'audio L\'ucio Do Val Lopes, Lucca Machado da Silva, Andr\'e de Oliveira Brand\~ao