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class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.10030">arXiv:2410.10030</a> <span> [<a href="https://arxiv.org/pdf/2410.10030">pdf</a>, <a href="https://arxiv.org/format/2410.10030">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"> A Step Towards Mixture of Grader: Statistical Analysis of Existing Automatic Evaluation Metrics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Soh%2C+Y+J">Yun Joon Soh</a>, <a href="/search/?searchtype=author&query=Zhao%2C+J">Jishen Zhao</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="2410.10030v1-abstract-short" style="display: inline;"> The explosion of open-sourced models and Question-Answering (QA) datasets emphasizes the importance of automated QA evaluation. We studied the statistics of the existing evaluation metrics for a better understanding of their limitations. By measuring the correlation coefficients of each evaluation metric concerning human-like evaluation score, we observed the following: (1) existing metrics have a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10030v1-abstract-full').style.display = 'inline'; document.getElementById('2410.10030v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.10030v1-abstract-full" style="display: none;"> The explosion of open-sourced models and Question-Answering (QA) datasets emphasizes the importance of automated QA evaluation. We studied the statistics of the existing evaluation metrics for a better understanding of their limitations. By measuring the correlation coefficients of each evaluation metric concerning human-like evaluation score, we observed the following: (1) existing metrics have a high correlation among them concerning the question type (e.g., single word, single phrase, etc.), (2) no single metric can adequately estimate the human-like evaluation. As a potential solution, we discuss how a Mixture Of Grader could potentially improve the auto QA evaluator quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10030v1-abstract-full').style.display = 'none'; document.getElementById('2410.10030v1-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">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/2409.13695">arXiv:2409.13695</a> <span> [<a href="https://arxiv.org/pdf/2409.13695">pdf</a>, <a href="https://arxiv.org/format/2409.13695">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="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> You Only Use Reactive Attention Slice For Long Context Retrieval </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Soh%2C+Y+J">Yun Joon Soh</a>, <a href="/search/?searchtype=author&query=Huang%2C+H">Hanxian Huang</a>, <a href="/search/?searchtype=author&query=Tian%2C+Y">Yuandong Tian</a>, <a href="/search/?searchtype=author&query=Zhao%2C+J">Jishen Zhao</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="2409.13695v1-abstract-short" style="display: inline;"> Supporting longer context for Large Language Models (LLM) is a promising direction to advance LLMs. As training a model for a longer context window is computationally expensive, many alternative solutions, such as Retrieval Augmented Generation (RAG), have been used. However, most existing RAG methods adopt embedding-based retrieval that falls short on long contexts. To address such challenges,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13695v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13695v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13695v1-abstract-full" style="display: none;"> Supporting longer context for Large Language Models (LLM) is a promising direction to advance LLMs. As training a model for a longer context window is computationally expensive, many alternative solutions, such as Retrieval Augmented Generation (RAG), have been used. However, most existing RAG methods adopt embedding-based retrieval that falls short on long contexts. To address such challenges, we propose an attention-based retrieval technique, You Only Use Reactive Attention slice (YOURA). YOURA leverages a novel retrieval heuristic called reaction score to rank the relevance of each sentence in the input context with the query sentence. Intuitively, we measure how the per-token attention score "reacts" to the query and greedily retrieves the most reactive sentences. Internally, YOURA generates a token-indexed vector (called reaction vector) for the whole input context. To map each sentence to the token-indexed vector, we propose an Embedding-Agnostic Sentence Yield (EASY), a best-effort token wiggling algorithm. We evaluate our retrieval technique on three open-source pre-trained LLM models across six LongBench QA datasets. Our technique achieves up to 30% vLLM inference throughput improvement for serving long-context queries with a nearly identical quality score to the simple yet effective truncate-middle approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13695v1-abstract-full').style.display = 'none'; document.getElementById('2409.13695v1-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> 3 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/2004.00243">arXiv:2004.00243</a> <span> [<a href="https://arxiv.org/pdf/2004.00243">pdf</a>, <a href="https://arxiv.org/format/2004.00243">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> </div> </div> <p class="title is-5 mathjax"> Efficient Implementation of Multi-Channel Convolution in Monolithic 3D ReRAM Crossbar </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ko%2C+S">Sho Ko</a>, <a href="/search/?searchtype=author&query=Soh%2C+Y+J">Yun Joon Soh</a>, <a href="/search/?searchtype=author&query=Zhao%2C+J">Jishen Zhao</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="2004.00243v1-abstract-short" style="display: inline;"> Convolutional neural networks (CNNs) demonstrate promising accuracy in a wide range of applications. Among all layers in CNNs, convolution layers are the most computation-intensive and consume the most energy. As the maturity of device and fabrication technology, 3D resistive random access memory (ReRAM) receives substantial attention for accelerating large vector-matrix multiplication and convolu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.00243v1-abstract-full').style.display = 'inline'; document.getElementById('2004.00243v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.00243v1-abstract-full" style="display: none;"> Convolutional neural networks (CNNs) demonstrate promising accuracy in a wide range of applications. Among all layers in CNNs, convolution layers are the most computation-intensive and consume the most energy. As the maturity of device and fabrication technology, 3D resistive random access memory (ReRAM) receives substantial attention for accelerating large vector-matrix multiplication and convolution due to its high parallelism and energy efficiency benefits. However, implementing multi-channel convolution naively in 3D ReRAM will either produce incorrect results or exploit only partial parallelism of 3D ReRAM. In this paper, we propose a 3D ReRAM-based convolution accelerator architecture, which efficiently maps multi-channel convolution to monolithic 3D ReRAM. Our design has two key principles. First, we exploit the intertwined structure of 3D ReRAM to implement multi-channel convolution by using a state-of-the-art convolution algorithm. Second, we propose a new approach to efficiently implement negative weights by separating them from non-negative weights using configurable interconnects. Our evaluation demonstrates that our mapping scheme in 16-layer 3D ReRAM achieves a speedup of 5.79X, 927.81X, and 36.8X compared with a custom 2D ReRAM baseline and state-of-the-art CPU and GPU. Our design also reduces energy consumption by 2.12X, 1802.64X, and 114.1X compared with the same baseline. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.00243v1-abstract-full').style.display = 'none'; document.getElementById('2004.00243v1-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> 1 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1903.05714">arXiv:1903.05714</a> <span> [<a href="https://arxiv.org/pdf/1903.05714">pdf</a>, <a href="https://arxiv.org/format/1903.05714">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</span> </div> </div> <p class="title is-5 mathjax"> Basic Performance Measurements of the Intel Optane DC Persistent Memory Module </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Izraelevitz%2C+J">Joseph Izraelevitz</a>, <a href="/search/?searchtype=author&query=Yang%2C+J">Jian Yang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+L">Lu Zhang</a>, <a href="/search/?searchtype=author&query=Kim%2C+J">Juno Kim</a>, <a href="/search/?searchtype=author&query=Liu%2C+X">Xiao Liu</a>, <a href="/search/?searchtype=author&query=Memaripour%2C+A">Amirsaman Memaripour</a>, <a href="/search/?searchtype=author&query=Soh%2C+Y+J">Yun Joon Soh</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zixuan Wang</a>, <a href="/search/?searchtype=author&query=Xu%2C+Y">Yi Xu</a>, <a href="/search/?searchtype=author&query=Dulloor%2C+S+R">Subramanya R. Dulloor</a>, <a href="/search/?searchtype=author&query=Zhao%2C+J">Jishen Zhao</a>, <a href="/search/?searchtype=author&query=Swanson%2C+S">Steven Swanson</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="1903.05714v3-abstract-short" style="display: inline;"> Scalable nonvolatile memory DIMMs will finally be commercially available with the release of the Intel Optane DC Persistent Memory Module (or just "Optane DC PMM"). This new nonvolatile DIMM supports byte-granularity accesses with access times on the order of DRAM, while also providing data storage that survives power outages. This work comprises the first in-depth, scholarly, performance review o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.05714v3-abstract-full').style.display = 'inline'; document.getElementById('1903.05714v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1903.05714v3-abstract-full" style="display: none;"> Scalable nonvolatile memory DIMMs will finally be commercially available with the release of the Intel Optane DC Persistent Memory Module (or just "Optane DC PMM"). This new nonvolatile DIMM supports byte-granularity accesses with access times on the order of DRAM, while also providing data storage that survives power outages. This work comprises the first in-depth, scholarly, performance review of Intel's Optane DC PMM, exploring its capabilities as a main memory device, and as persistent, byte-addressable memory exposed to user-space applications. This report details the technologies performance under a number of modes and scenarios, and across a wide variety of macro-scale benchmarks. Optane DC PMMs can be used as large memory devices with a DRAM cache to hide their lower bandwidth and higher latency. When used in this Memory (or cached) mode, Optane DC memory has little impact on applications with small memory footprints. Applications with larger memory footprints may experience some slow-down relative to DRAM, but are now able to keep much more data in memory. When used under a file system, Optane DC PMMs can result in significant performance gains, especially when the file system is optimized to use the load/store interface of the Optane DC PMM and the application uses many small, persistent writes. For instance, using the NOVA-relaxed NVMM file system, we can improve the performance of Kyoto Cabinet by almost 2x. Optane DC PMMs can also enable user-space persistence where the application explicitly controls its writes into persistent Optane DC media. In our experiments, modified applications that used user-space Optane DC persistence generally outperformed their file system counterparts. For instance, the persistent version of RocksDB performed almost 2x faster than the equivalent program utilizing an NVMM-aware file system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.05714v3-abstract-full').style.display = 'none'; document.getElementById('1903.05714v3-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 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2019. </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 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