iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries

ArXiv Pub Date : 2024-03-07 DOI:10.1145/3640543.3645142
Adam Joseph Coscia, Langdon Holmes, Wesley Morris, Joon Suh Choi, Scott Crossley, A. Endert
{"title":"iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries","authors":"Adam Joseph Coscia, Langdon Holmes, Wesley Morris, Joon Suh Choi, Scott Crossley, A. Endert","doi":"10.1145/3640543.3645142","DOIUrl":null,"url":null,"abstract":"The recent explosion in popularity of large language models (LLMs) has inspired learning engineers to incorporate them into adaptive educational tools that automatically score summary writing. Understanding and evaluating LLMs is vital before deploying them in critical learning environments, yet their unprecedented size and expanding number of parameters inhibits transparency and impedes trust when they underperform. Through a collaborative user-centered design process with several learning engineers building and deploying summary scoring LLMs, we characterized fundamental design challenges and goals around interpreting their models, including aggregating large text inputs, tracking score provenance, and scaling LLM interpretability methods. To address their concerns, we developed iScore, an interactive visual analytics tool for learning engineers to upload, score, and compare multiple summaries simultaneously. Tightly integrated views allow users to iteratively revise the language in summaries, track changes in the resulting LLM scores, and visualize model weights at multiple levels of abstraction. To validate our approach, we deployed iScore with three learning engineers over the course of a month. We present a case study where interacting with iScore led a learning engineer to improve their LLM's score accuracy by three percentage points. Finally, we conducted qualitative interviews with the learning engineers that revealed how iScore enabled them to understand, evaluate, and build trust in their LLMs during deployment.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"22 40","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ArXiv","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3640543.3645142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The recent explosion in popularity of large language models (LLMs) has inspired learning engineers to incorporate them into adaptive educational tools that automatically score summary writing. Understanding and evaluating LLMs is vital before deploying them in critical learning environments, yet their unprecedented size and expanding number of parameters inhibits transparency and impedes trust when they underperform. Through a collaborative user-centered design process with several learning engineers building and deploying summary scoring LLMs, we characterized fundamental design challenges and goals around interpreting their models, including aggregating large text inputs, tracking score provenance, and scaling LLM interpretability methods. To address their concerns, we developed iScore, an interactive visual analytics tool for learning engineers to upload, score, and compare multiple summaries simultaneously. Tightly integrated views allow users to iteratively revise the language in summaries, track changes in the resulting LLM scores, and visualize model weights at multiple levels of abstraction. To validate our approach, we deployed iScore with three learning engineers over the course of a month. We present a case study where interacting with iScore led a learning engineer to improve their LLM's score accuracy by three percentage points. Finally, we conducted qualitative interviews with the learning engineers that revealed how iScore enabled them to understand, evaluate, and build trust in their LLMs during deployment.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
iScore:解读语言模型如何为摘要自动评分的可视化分析技术
最近,大型语言模型(LLMs)大受追捧,这激发了学习工程师们将其纳入自动为摘要写作评分的自适应教育工具的热情。在关键的学习环境中部署 LLMs 之前,了解和评估 LLMs 至关重要,然而 LLMs 前所未有的规模和不断增加的参数数量阻碍了其透明度,并在表现不佳时妨碍了信任。通过与几位正在构建和部署摘要评分 LLM 的学习工程师开展以用户为中心的协作设计过程,我们确定了围绕解释其模型的基本设计挑战和目标,包括聚合大量文本输入、跟踪分数来源和扩展 LLM 可解释性方法。为了解决他们所关心的问题,我们开发了 iScore,这是一款交互式可视化分析工具,可供学习工程师同时上传、评分和比较多个摘要。紧密集成的视图允许用户迭代修改摘要中的语言,跟踪所产生的 LLM 分数的变化,并在多个抽象层次上可视化模型权重。为了验证我们的方法,我们与三位学习工程师一起部署了 iScore,历时一个月。我们介绍了一个案例研究,通过与 iScore 的互动,一位学习工程师将他们的 LLM 分数准确率提高了三个百分点。最后,我们对学习工程师进行了定性访谈,揭示了 iScore 如何帮助他们在部署过程中理解、评估 LLM 并建立信任。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization TinyGC-Net: An Extremely Tiny Network for Calibrating MEMS Gyroscopes Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints F2Depth: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis Efficient Constrained k-Center Clustering with Background Knowledge
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1