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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Liu, J H"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" 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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/2411.04905">arXiv:2411.04905</a> <span> [<a href="https://arxiv.org/pdf/2411.04905">pdf</a>, <a href="https://arxiv.org/format/2411.04905">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="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huang%2C+S">Siming Huang</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+T">Tianhao Cheng</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J+K">J. K. Liu</a>, <a href="/search/cs?searchtype=author&query=Hao%2C+J">Jiaran Hao</a>, <a href="/search/cs?searchtype=author&query=Song%2C+L">Liuyihan Song</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+Y">Yang Xu</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+J">J. Yang</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J+H">J. H. Liu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+C">Chenchen Zhang</a>, <a href="/search/cs?searchtype=author&query=Chai%2C+L">Linzheng Chai</a>, <a href="/search/cs?searchtype=author&query=Yuan%2C+R">Ruifeng Yuan</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zhaoxiang Zhang</a>, <a href="/search/cs?searchtype=author&query=Fu%2C+J">Jie Fu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Q">Qian Liu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+G">Ge Zhang</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zili Wang</a>, <a href="/search/cs?searchtype=author&query=Qi%2C+Y">Yuan Qi</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+Y">Yinghui Xu</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+W">Wei Chu</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="2411.04905v2-abstract-short" style="display: inline;"> Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs suitable for rigorous scientific investigation, particularly those with reproducible data processing pipelines and transparent t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04905v2-abstract-full').style.display = 'inline'; document.getElementById('2411.04905v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.04905v2-abstract-full" style="display: none;"> Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs suitable for rigorous scientific investigation, particularly those with reproducible data processing pipelines and transparent training protocols, remain limited. The scarcity is due to various challenges, including resource constraints, ethical considerations, and the competitive advantages of keeping models advanced. To address the gap, we introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community. Unlike most prior efforts, we release not only model weights and inference code, but also the reproducible training data, complete data processing pipeline, rigorous experimental ablation results, and detailed training protocols for open scientific research. Through this comprehensive release, we identify the key ingredients for building a top-tier code LLM: (1) code optimized heuristic rules for data cleaning and methods for data deduplication, (2) recall of text corpus related to code and (3) high-quality synthetic data in both annealing and supervised fine-tuning stages. By offering this level of openness, we aim to broaden access to all aspects of a top-tier code LLM, with OpenCoder serving as both a powerful model and an open foundation to accelerate research, and enable reproducible advancements in code AI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04905v2-abstract-full').style.display = 'none'; document.getElementById('2411.04905v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.13639">arXiv:2410.13639</a> <span> [<a href="https://arxiv.org/pdf/2410.13639">pdf</a>, <a href="https://arxiv.org/format/2410.13639">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"> A Comparative Study on Reasoning Patterns of OpenAI's o1 Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+S">Siwei Wu</a>, <a href="/search/cs?searchtype=author&query=Peng%2C+Z">Zhongyuan Peng</a>, <a href="/search/cs?searchtype=author&query=Du%2C+X">Xinrun Du</a>, <a href="/search/cs?searchtype=author&query=Zheng%2C+T">Tuney Zheng</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+M">Minghao Liu</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+J">Jialong Wu</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+J">Jiachen Ma</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yizhi Li</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+J">Jian Yang</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+W">Wangchunshu Zhou</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+Q">Qunshu Lin</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+J">Junbo Zhao</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zhaoxiang Zhang</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+W">Wenhao Huang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+G">Ge Zhang</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+C">Chenghua Lin</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J+H">J. H. Liu</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="2410.13639v2-abstract-short" style="display: inline;"> Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, merely increasing the number of model parameters yields diminishing performance improvements and heavy computational costs. Recently, OpenAI's o1 model has shown that inference strategies (i.e., Test-time Compute methods) c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13639v2-abstract-full').style.display = 'inline'; document.getElementById('2410.13639v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.13639v2-abstract-full" style="display: none;"> Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, merely increasing the number of model parameters yields diminishing performance improvements and heavy computational costs. Recently, OpenAI's o1 model has shown that inference strategies (i.e., Test-time Compute methods) can also significantly enhance the reasoning capabilities of LLMs. However, the mechanisms behind these methods are still unexplored. In our work, to investigate the reasoning patterns of o1, we compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general reasoning benchmarks in three domains (i.e., math, coding, commonsense reasoning). Specifically, first, our experiments show that the o1 model has achieved the best performance on most datasets. Second, as for the methods of searching diverse responses (e.g., BoN), we find the reward models' capability and the search space both limit the upper boundary of these methods. Third, as for the methods that break the problem into many sub-problems, the Agent Workflow has achieved better performance than Step-wise BoN due to the domain-specific system prompt for planning better reasoning processes. Fourth, it is worth mentioning that we have summarized six reasoning patterns of o1, and provided a detailed analysis on several reasoning benchmarks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13639v2-abstract-full').style.display = 'none'; document.getElementById('2410.13639v2-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> 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.06851">arXiv:2409.06851</a> <span> [<a href="https://arxiv.org/pdf/2409.06851">pdf</a>, <a href="https://arxiv.org/format/2409.06851">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> </div> </div> <p class="title is-5 mathjax"> LIME: Less Is More for MLLM Evaluation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhu%2C+K">King Zhu</a>, <a href="/search/cs?searchtype=author&query=Zang%2C+Q">Qianbo Zang</a>, <a href="/search/cs?searchtype=author&query=Jia%2C+S">Shian Jia</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+S">Siwei Wu</a>, <a href="/search/cs?searchtype=author&query=Fang%2C+F">Feiteng Fang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yizhi Li</a>, <a href="/search/cs?searchtype=author&query=Gavin%2C+S">Shawn Gavin</a>, <a href="/search/cs?searchtype=author&query=Zheng%2C+T">Tuney Zheng</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+J">Jiawei Guo</a>, <a href="/search/cs?searchtype=author&query=Li%2C+B">Bo Li</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Haoning Wu</a>, <a href="/search/cs?searchtype=author&query=Qu%2C+X">Xingwei Qu</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+J">Jian Yang</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zachary Liu</a>, <a href="/search/cs?searchtype=author&query=Yue%2C+X">Xiang Yue</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J+H">J. H. Liu</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+C">Chenghua Lin</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+M">Min Yang</a>, <a href="/search/cs?searchtype=author&query=Ni%2C+S">Shiwen Ni</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+W">Wenhao Huang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+G">Ge Zhang</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="2409.06851v3-abstract-short" style="display: inline;"> Multimodal Large Language Models (MLLMs) are evaluated on various benchmarks, such as image captioning, visual question answering, and reasoning. However, many of these benchmarks include overly simple or uninformative samples, complicating the effective distinction of different MLLMs' performance. Furthermore, evaluating models across numerous benchmarks incurs a significant computational burden.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06851v3-abstract-full').style.display = 'inline'; document.getElementById('2409.06851v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.06851v3-abstract-full" style="display: none;"> Multimodal Large Language Models (MLLMs) are evaluated on various benchmarks, such as image captioning, visual question answering, and reasoning. However, many of these benchmarks include overly simple or uninformative samples, complicating the effective distinction of different MLLMs' performance. Furthermore, evaluating models across numerous benchmarks incurs a significant computational burden. To address these issues, we propose LIME (Less Is More for MLLM Evaluation), a refined and efficient benchmark curated through a semi-automated pipeline. This pipeline filters out uninformative samples and eliminates answer leakage by focusing on tasks that necessitate image-based understanding. Our experiments indicate that LIME reduces the number of samples by 76% and evaluation time by 77%, while also providing a more effective means of distinguishing the capabilities of different models. Notably, we find that traditional automatic metrics, such as CIDEr, are inadequate for assessing MLLMs' captioning performance; excluding the caption task score yields a more accurate reflection of overall model performance. All code and data are available at https://github.com/kangreen0210/LIME. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06851v3-abstract-full').style.display = 'none'; document.getElementById('2409.06851v3-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> 13 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 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/2407.17379">arXiv:2407.17379</a> <span> [<a href="https://arxiv.org/pdf/2407.17379">pdf</a>, <a href="https://arxiv.org/format/2407.17379">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="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+S">Siwei Wu</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+K">Kang Zhu</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+Y">Yu Bai</a>, <a href="/search/cs?searchtype=author&query=Liang%2C+Y">Yiming Liang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yizhi Li</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Haoning Wu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J+H">J. H. Liu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+R">Ruibo Liu</a>, <a href="/search/cs?searchtype=author&query=Qu%2C+X">Xingwei Qu</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+X">Xuxin Cheng</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+G">Ge Zhang</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+W">Wenhao Huang</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+C">Chenghua Lin</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="2407.17379v2-abstract-short" style="display: inline;"> Given the remarkable success that large visual language models (LVLMs) have achieved in image perception tasks, the endeavor to make LVLMs perceive the world like humans is drawing increasing attention. Current multi-modal benchmarks primarily focus on facts or specific topic-related knowledge contained within individual images. However, they often overlook the associative relations between multip… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.17379v2-abstract-full').style.display = 'inline'; document.getElementById('2407.17379v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.17379v2-abstract-full" style="display: none;"> Given the remarkable success that large visual language models (LVLMs) have achieved in image perception tasks, the endeavor to make LVLMs perceive the world like humans is drawing increasing attention. Current multi-modal benchmarks primarily focus on facts or specific topic-related knowledge contained within individual images. However, they often overlook the associative relations between multiple images, which require the identification and analysis of similarities among entities or content present in different images. Therefore, we propose the multi-image relation association task and a meticulously curated Multi-granularity Multi-image Relational Association (MMRA) benchmark, comprising 1,024 samples. In order to systematically and comprehensively evaluate current LVLMs, we establish an associational relation system among images that contain 11 subtasks (e.g, UsageSimilarity, SubEvent) at two granularity levels (i.e., image and entity) according to the relations in ConceptNet. Our experiments reveal that on the MMRA benchmark, current multi-image LVLMs exhibit distinct advantages and disadvantages across various subtasks. Notably, fine-grained, entity-level multi-image perception tasks pose a greater challenge for LVLMs compared to image-level tasks. Moreover, LVLMs perform poorly on spatial-related tasks, indicating that LVLMs still have limited spatial awareness. Additionally, our findings indicate that while LVLMs demonstrate a strong capability to perceive image details, enhancing their ability to associate information across multiple images hinges on improving the reasoning capabilities of their language model component. Moreover, we explored the ability of LVLMs to perceive image sequences within the context of our multi-image association task. Our experiments show that the majority of current LVLMs do not adequately model image sequences during the pre-training process. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.17379v2-abstract-full').style.display = 'none'; document.getElementById('2407.17379v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">VLMs, Multi-Image Association</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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