The paper introduces PLUS, a framework that uses reinforcement learning to generate text-based summaries of individual users’ preferences, characteristics, and past conversations. These summaries condition a reward model, allowing it to predict personalized response preferences and improving reward accuracy by 11–77 % over the standard Bradley‑Terry model. PLUS demonstrates robust performance with new users and topics, achieves a 25 % improvement over existing personalized RLHF techniques, and enables zero‑shot personalization for state‑of‑the‑art models like GPT‑4.
By Hyunji Nam, Yanming Wan, Mickel Liu, Peter Ahnn, Jianxun Lian, Natasha Jaques
arXiv:2606. 03867v1 Announce Type: cross Abstract: Multi-Document Summarization (MDS) plays a critical role in distilling essential information from collections of textual data.
By Cuong Vuong Tuan, Trang Mai Xuan, Tien-Cuong Nguyen, Vu-Duc Ngo, Thien Van Luong
We trained “critique-writing” models to describe flaws in summaries. Human evaluators find flaws in summaries much more often when shown our model’s critiques.
We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization—all without task-specific training.
arXiv:2606. 08000v1 Announce Type: cross Abstract: The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summarization remains an open research problem.
By Dongqi Liu, Chenxi Whitehouse, Zheng Zhao, Zhuchen Cao, Jian Li, Yabiao Wang
arXiv:2606. 08445v1 Announce Type: cross Abstract: Meeting documents are challenging to summarize due to their length and complex conversational structure.
By Sangwon Ryu, Heejin Do, Jun Seo, Daehui Kim, Yunsu Kim, Gary Geunbae Lee, Jungseul Ok
arXiv:2608. 19200v1 Announce Type: cross Abstract: Text summarization refers to the task of condensing a document into a shorter version while preserving its key information.
By Daisy Aptovska, Vinayak Elangovan
arXiv:2607. 10806v1 Announce Type: cross Abstract: Quantifying abstractiveness in generated summaries is essential for evaluating summarization models beyond surface-level metrics like ROUGE.
By Praveenkumar Katwe, Rakesh Chandra Balabantaray, Kali Prasad Vittala
The PSK submission to the WMT 2026 Multilingual Instruction Shared Task employs a 3.35B‑parameter Tiny Aya Global model enhanced with three QLoRA adapters, each dedicated to a specific task: multilingual summarization, passage‑based question answering, and filtered standalone question answering. The summarization adapter is trained on multilingual document‑summary pairs, including scientific papers with author‑written abstracts, and outperforms a multitask adapter trained solely on organizer data on a held‑out split. For open question answering, results vary with answer length and evaluation method, prompting the submission of three systems that share the same context and summarization adapters but differ in their open‑QA adapters.
By Srikar Kashyap Pulipaka
arXiv:2503. 02368v4 Announce Type: replace-cross Abstract: While guided decoding, especially value-guided methods, has emerged as a cost-effective alternative for controlling language model outputs without re-training models, its effectiveness is limited by the accuracy of the value function.
By Zhenhua Liu, Lijun Li, Ruizhe Chen, Yuxian Jiang, Tong Zhu, Zhaochen Su, Wenliang Chen, Jing Shao
arXiv:2608. 04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult.
By Karen Lee, Dhanashree Balaram, Seojun Shon, Umair Rasheed