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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.14834">arXiv:2502.14834</a> <span> [<a href="https://arxiv.org/pdf/2502.14834">pdf</a>, <a href="https://arxiv.org/format/2502.14834">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tu%2C+S">Shangqing Tu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yucheng Wang</a>, <a href="/search/cs?searchtype=author&query=Zhang-Li%2C+D">Daniel Zhang-Li</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+Y">Yushi Bai</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jifan Yu</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Yuhao Wu</a>, <a href="/search/cs?searchtype=author&query=Hou%2C+L">Lei Hou</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+H">Huiqin Liu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zhiyuan Liu</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+B">Bin Xu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Juanzi Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.14834v1-abstract-short" style="display: inline;"> Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000 words. We find that the primary limitation is the absence of long output examples during supervised fine-tuning (SFT). To tackle this issue, we introduce LongWriter-V-22k, a SFT dataset comprising 22,158 examples, each… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.14834v1-abstract-full').style.display = 'inline'; document.getElementById('2502.14834v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.14834v1-abstract-full" style="display: none;"> Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000 words. We find that the primary limitation is the absence of long output examples during supervised fine-tuning (SFT). To tackle this issue, we introduce LongWriter-V-22k, a SFT dataset comprising 22,158 examples, each with multiple input images, an instruction, and corresponding outputs ranging from 0 to 10,000 words. Moreover, to achieve long outputs that maintain high-fidelity to the input images, we employ Direct Preference Optimization (DPO) to the SFT model. Given the high cost of collecting human feedback for lengthy outputs (e.g., 3,000 words), we propose IterDPO, which breaks long outputs into segments and uses iterative corrections to form preference pairs with the original outputs. Additionally, we develop MMLongBench-Write, a benchmark featuring six tasks to evaluate the long-generation capabilities of VLMs. Our 7B parameter model, trained with LongWriter-V-22k and IterDPO, achieves impressive performance on this benchmark, outperforming larger proprietary models like GPT-4o. Code and data: https://github.com/THU-KEG/LongWriter-V <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.14834v1-abstract-full').style.display = 'none'; document.getElementById('2502.14834v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.07372">arXiv:2409.07372</a> <span> [<a href="https://arxiv.org/pdf/2409.07372">pdf</a>, <a href="https://arxiv.org/format/2409.07372">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang-Li%2C+D">Daniel Zhang-Li</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jifan Yu</a>, <a href="/search/cs?searchtype=author&query=Yin%2C+J+L+J">Joy Lim Jia Yin</a>, <a href="/search/cs?searchtype=author&query=Tu%2C+S">Shangqing Tu</a>, <a href="/search/cs?searchtype=author&query=Gong%2C+L">Linlu Gong</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+H">Haohua Wang</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zhiyuan Liu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+H">Huiqin Liu</a>, <a href="/search/cs?searchtype=author&query=Hou%2C+L">Lei Hou</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Juanzi Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.07372v1-abstract-short" style="display: inline;"> The vast pre-existing slides serve as rich and important materials to carry lecture knowledge. However, effectively leveraging lecture slides to serve students is difficult due to the multi-modal nature of slide content and the heterogeneous teaching actions. We study the problem of discovering effective designs that convert a slide into an interactive lecture. We develop Slide2Lecture, a tuning-f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07372v1-abstract-full').style.display = 'inline'; document.getElementById('2409.07372v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.07372v1-abstract-full" style="display: none;"> The vast pre-existing slides serve as rich and important materials to carry lecture knowledge. However, effectively leveraging lecture slides to serve students is difficult due to the multi-modal nature of slide content and the heterogeneous teaching actions. We study the problem of discovering effective designs that convert a slide into an interactive lecture. We develop Slide2Lecture, a tuning-free and knowledge-regulated intelligent tutoring system that can (1) effectively convert an input lecture slide into a structured teaching agenda consisting of a set of heterogeneous teaching actions; (2) create and manage an interactive lecture that generates responsive interactions catering to student learning demands while regulating the interactions to follow teaching actions. Slide2Lecture contains a complete pipeline for learners to obtain an interactive classroom experience to learn the slide. For teachers and developers, Slide2Lecture enables customization to cater to personalized demands. The evaluation rated by annotators and students shows that Slide2Lecture is effective in outperforming the remaining implementation. Slide2Lecture's online deployment has made more than 200K interaction with students in the 3K lecture sessions. We open source Slide2Lecture's implementation in https://anonymous.4open.science/r/slide2lecture-4210/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07372v1-abstract-full').style.display = 'none'; document.getElementById('2409.07372v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.03512">arXiv:2409.03512</a> <span> [<a href="https://arxiv.org/pdf/2409.03512">pdf</a>, <a href="https://arxiv.org/format/2409.03512">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jifan Yu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhang-li%2C+D">Daniel Zhang-li</a>, <a href="/search/cs?searchtype=author&query=Tu%2C+S">Shangqing Tu</a>, <a href="/search/cs?searchtype=author&query=Hao%2C+Z">Zhanxin Hao</a>, <a href="/search/cs?searchtype=author&query=Li%2C+R+M">Rui Miao Li</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Haoxuan Li</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yuanchun Wang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Hanming Li</a>, <a href="/search/cs?searchtype=author&query=Gong%2C+L">Linlu Gong</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+J">Jie Cao</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+J">Jiayin Lin</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+J">Jinchang Zhou</a>, <a href="/search/cs?searchtype=author&query=Qin%2C+F">Fei Qin</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+H">Haohua Wang</a>, <a href="/search/cs?searchtype=author&query=Jiang%2C+J">Jianxiao Jiang</a>, <a href="/search/cs?searchtype=author&query=Deng%2C+L">Lijun Deng</a>, <a href="/search/cs?searchtype=author&query=Zhan%2C+Y">Yisi Zhan</a>, <a href="/search/cs?searchtype=author&query=Xiao%2C+C">Chaojun Xiao</a>, <a href="/search/cs?searchtype=author&query=Dai%2C+X">Xusheng Dai</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+X">Xuan Yan</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+N">Nianyi Lin</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+N">Nan Zhang</a>, <a href="/search/cs?searchtype=author&query=Ni%2C+R">Ruixin Ni</a>, <a href="/search/cs?searchtype=author&query=Dang%2C+Y">Yang Dang</a> , et al. (8 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.03512v1-abstract-short" style="display: inline;"> Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to reach a broader audience has sparked extensive discussion and widespread adoption. Recognizing that personalized learning still holds significant potential for improvement, new AI technologies have been continuously integ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.03512v1-abstract-full').style.display = 'inline'; document.getElementById('2409.03512v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.03512v1-abstract-full" style="display: none;"> Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to reach a broader audience has sparked extensive discussion and widespread adoption. Recognizing that personalized learning still holds significant potential for improvement, new AI technologies have been continuously integrated into this learning format, resulting in a variety of educational AI applications such as educational recommendation and intelligent tutoring. The emergence of intelligence in large language models (LLMs) has allowed for these educational enhancements to be built upon a unified foundational model, enabling deeper integration. In this context, we propose MAIC (Massive AI-empowered Course), a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom, balancing scalability with adaptivity. Beyond exploring the conceptual framework and technical innovations, we conduct preliminary experiments at Tsinghua University, one of China's leading universities. Drawing from over 100,000 learning records of more than 500 students, we obtain a series of valuable observations and initial analyses. This project will continue to evolve, ultimately aiming to establish a comprehensive open platform that supports and unifies research, technology, and applications in exploring the possibilities of online education in the era of large model AI. We envision this platform as a collaborative hub, bringing together educators, researchers, and innovators to collectively explore the future of AI-driven online education. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.03512v1-abstract-full').style.display = 'none'; document.getElementById('2409.03512v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.19226">arXiv:2406.19226</a> <span> [<a href="https://arxiv.org/pdf/2406.19226">pdf</a>, <a href="https://arxiv.org/format/2406.19226">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Simulating Classroom Education with LLM-Empowered Agents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhang-Li%2C+D">Daniel Zhang-Li</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jifan Yu</a>, <a href="/search/cs?searchtype=author&query=Gong%2C+L">Linlu Gong</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+J">Jinchang Zhou</a>, <a href="/search/cs?searchtype=author&query=Hao%2C+Z">Zhanxin Hao</a>, <a href="/search/cs?searchtype=author&query=Jiang%2C+J">Jianxiao Jiang</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+J">Jie Cao</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+H">Huiqin Liu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zhiyuan Liu</a>, <a href="/search/cs?searchtype=author&query=Hou%2C+L">Lei Hou</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Juanzi Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.19226v2-abstract-short" style="display: inline;"> Large language models (LLMs) have been applied across various intelligent educational tasks to assist teaching. While preliminary studies have focused on task-specific, independent LLM-empowered agents, the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. In this work, we propose SimClass, a multi-agent classro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.19226v2-abstract-full').style.display = 'inline'; document.getElementById('2406.19226v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.19226v2-abstract-full" style="display: none;"> Large language models (LLMs) have been applied across various intelligent educational tasks to assist teaching. While preliminary studies have focused on task-specific, independent LLM-empowered agents, the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. In this work, we propose SimClass, a multi-agent classroom simulation teaching framework. We recognize representative class roles and introduce a novel class control mechanism for automatic classroom teaching, and conduct user experiments in two real-world courses. Using the Flanders Interactive Analysis System and Community of Inquiry theoretical frameworks from educational analysis, we demonstrate that LLMs can simulate a dynamic learning environment for users with active teacher-student and student-student interactions. We also observe group behaviors among agents in SimClass, where agents collaborate to create enlivening interactions in classrooms to improve user learning process. We hope this work pioneers the application of LLM-empowered multi-agent systems in virtual classroom teaching. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.19226v2-abstract-full').style.display = 'none'; document.getElementById('2406.19226v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.19318">arXiv:2403.19318</a> <span> [<a href="https://arxiv.org/pdf/2403.19318">pdf</a>, <a href="https://arxiv.org/format/2403.19318">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xiaokang Zhang</a>, <a href="/search/cs?searchtype=author&query=Luo%2C+S">Sijia Luo</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+B">Bohan Zhang</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+Z">Zeyao Ma</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jing Zhang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yang Li</a>, <a href="/search/cs?searchtype=author&query=Li%2C+G">Guanlin Li</a>, <a href="/search/cs?searchtype=author&query=Yao%2C+Z">Zijun Yao</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+K">Kangli Xu</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+J">Jinchang Zhou</a>, <a href="/search/cs?searchtype=author&query=Zhang-Li%2C+D">Daniel Zhang-Li</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jifan Yu</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+S">Shu Zhao</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Juanzi Li</a>, <a href="/search/cs?searchtype=author&query=Tang%2C+J">Jie Tang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.19318v3-abstract-short" style="display: inline;"> We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. We propose a distant supervision method for training, which comprises a reasoning process extension strategy, aiding in training LLMs to und… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19318v3-abstract-full').style.display = 'inline'; document.getElementById('2403.19318v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.19318v3-abstract-full" style="display: none;"> We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. We propose a distant supervision method for training, which comprises a reasoning process extension strategy, aiding in training LLMs to understand reasoning patterns more effectively as well as a cross-way validation strategy, ensuring the quality of the automatically generated data. To evaluate the performance of TableLLM, we have crafted benchmarks tailored to address both document and spreadsheet formats as well as constructed a well-organized evaluation pipeline capable of handling both scenarios. Thorough evaluations underscore the advantages of TableLLM when compared to various existing general-purpose and tabular data-focused LLMs. We have publicly released the model checkpoint, source code, benchmarks, and a web application for user interaction. Our codes and data are publicly available at https://github.com/TableLLM/TableLLM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19318v3-abstract-full').style.display = 'none'; document.getElementById('2403.19318v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">https://tablellm.github.io/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.05845">arXiv:2403.05845</a> <span> [<a href="https://arxiv.org/pdf/2403.05845">pdf</a>, <a href="https://arxiv.org/format/2403.05845">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Reverse That Number! Decoding Order Matters in Arithmetic Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang-Li%2C+D">Daniel Zhang-Li</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+N">Nianyi Lin</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jifan Yu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/cs?searchtype=author&query=Yao%2C+Z">Zijun Yao</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xiaokang Zhang</a>, <a href="/search/cs?searchtype=author&query=Hou%2C+L">Lei Hou</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jing Zhang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Juanzi Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.05845v1-abstract-short" style="display: inline;"> Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations. However, despite acknowledging the significance of digit order in arithmetic computation, current methodologies predominantly rely on sequential, step-by-step approaches for teaching LLMs arithmetic, resulting in a conclusion where obtaini… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05845v1-abstract-full').style.display = 'inline'; document.getElementById('2403.05845v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.05845v1-abstract-full" style="display: none;"> Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations. However, despite acknowledging the significance of digit order in arithmetic computation, current methodologies predominantly rely on sequential, step-by-step approaches for teaching LLMs arithmetic, resulting in a conclusion where obtaining better performance involves fine-grained step-by-step. Diverging from this conventional path, our work introduces a novel strategy that not only reevaluates the digit order by prioritizing output from the least significant digit but also incorporates a step-by-step methodology to substantially reduce complexity. We have developed and applied this method in a comprehensive set of experiments. Compared to the previous state-of-the-art (SOTA) method, our findings reveal an overall improvement of in accuracy while requiring only a third of the tokens typically used during training. For the purpose of facilitating replication and further research, we have made our code and dataset publicly available at \url{https://anonymous.4open.science/r/RAIT-9FB7/}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05845v1-abstract-full').style.display = 'none'; document.getElementById('2403.05845v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.09296">arXiv:2306.09296</a> <span> [<a href="https://arxiv.org/pdf/2306.09296">pdf</a>, <a href="https://arxiv.org/format/2306.09296">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> KoLA: Carefully Benchmarking World Knowledge of Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jifan Yu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+X">Xiaozhi Wang</a>, <a href="/search/cs?searchtype=author&query=Tu%2C+S">Shangqing Tu</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+S">Shulin Cao</a>, <a href="/search/cs?searchtype=author&query=Zhang-Li%2C+D">Daniel Zhang-Li</a>, <a href="/search/cs?searchtype=author&query=Lv%2C+X">Xin Lv</a>, <a href="/search/cs?searchtype=author&query=Peng%2C+H">Hao Peng</a>, <a href="/search/cs?searchtype=author&query=Yao%2C+Z">Zijun Yao</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xiaohan Zhang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Hanming Li</a>, <a href="/search/cs?searchtype=author&query=Li%2C+C">Chunyang Li</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+Y">Yushi Bai</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yantao Liu</a>, <a href="/search/cs?searchtype=author&query=Xin%2C+A">Amy Xin</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+N">Nianyi Lin</a>, <a href="/search/cs?searchtype=author&query=Yun%2C+K">Kaifeng Yun</a>, <a href="/search/cs?searchtype=author&query=Gong%2C+L">Linlu Gong</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Jianhui Chen</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Z">Zhili Wu</a>, <a href="/search/cs?searchtype=author&query=Qi%2C+Y">Yunjia Qi</a>, <a href="/search/cs?searchtype=author&query=Li%2C+W">Weikai Li</a>, <a href="/search/cs?searchtype=author&query=Guan%2C+Y">Yong Guan</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+K">Kaisheng Zeng</a>, <a href="/search/cs?searchtype=author&query=Qi%2C+J">Ji Qi</a> , et al. (10 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.09296v3-abstract-short" style="display: inline;"> The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticulous and thoughtful designs are essential to thorough, unbiased, and applicable evaluations. Given the importance of world knowledge to LLMs, we construct a Knowledge-oriented LLM Assessment benchmark (KoLA), in which we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09296v3-abstract-full').style.display = 'inline'; document.getElementById('2306.09296v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.09296v3-abstract-full" style="display: none;"> The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticulous and thoughtful designs are essential to thorough, unbiased, and applicable evaluations. Given the importance of world knowledge to LLMs, we construct a Knowledge-oriented LLM Assessment benchmark (KoLA), in which we carefully design three crucial factors: (1) For \textbf{ability modeling}, we mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering $19$ tasks. (2) For \textbf{data}, to ensure fair comparisons, we use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, aiming to evaluate the capacity to handle unseen data and evolving knowledge. (3) For \textbf{evaluation criteria}, we adopt a contrastive system, including overall standard scores for better numerical comparability across tasks and models and a unique self-contrast metric for automatically evaluating knowledge-creating ability. We evaluate $28$ open-source and commercial LLMs and obtain some intriguing findings. The KoLA dataset and open-participation leaderboard are publicly released at https://kola.xlore.cn and will be continuously updated to provide references for developing LLMs and knowledge-related systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09296v3-abstract-full').style.display = 'none'; document.getElementById('2306.09296v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by ICLR 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.14401">arXiv:2302.14401</a> <span> [<a href="https://arxiv.org/pdf/2302.14401">pdf</a>, <a href="https://arxiv.org/format/2302.14401">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jing Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xiaokang Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhang-Li%2C+D">Daniel Zhang-Li</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jifan Yu</a>, <a href="/search/cs?searchtype=author&query=Yao%2C+Z">Zijun Yao</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+Z">Zeyao Ma</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+Y">Yiqi Xu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+H">Haohua Wang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xiaohan Zhang</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+N">Nianyi Lin</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+S">Sunrui Lu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Juanzi Li</a>, <a href="/search/cs?searchtype=author&query=Tang%2C+J">Jie Tang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.14401v1-abstract-short" style="display: inline;"> We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. GLM-Dialog offers a series of applicable techniques for exploiting various external knowledge including both helpful and noisy knowledge, enabling the creation of robust knowledge-grounded dialogue LLMs with limi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.14401v1-abstract-full').style.display = 'inline'; document.getElementById('2302.14401v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.14401v1-abstract-full" style="display: none;"> We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. GLM-Dialog offers a series of applicable techniques for exploiting various external knowledge including both helpful and noisy knowledge, enabling the creation of robust knowledge-grounded dialogue LLMs with limited proper datasets. To evaluate the GLM-Dialog more fairly, we also propose a novel evaluation method to allow humans to converse with multiple deployed bots simultaneously and compare their performance implicitly instead of explicitly rating using multidimensional metrics.Comprehensive evaluations from automatic to human perspective demonstrate the advantages of GLM-Dialog comparing with existing open source Chinese dialogue models. We release both the model checkpoint and source code, and also deploy it as a WeChat application to interact with users. We offer our evaluation platform online in an effort to prompt the development of open source models and reliable dialogue evaluation systems. The additional easy-to-use toolkit that consists of short text entity linking, query generation, and helpful knowledge classification is also released to enable diverse applications. All the source code is available on Github. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.14401v1-abstract-full').style.display = 'none'; document.getElementById('2302.14401v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a 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