iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911791012904960 |
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| author | Coscia, Adam Holmes, Langdon Morris, Wesley Choi, Joon Suh Crossley, Scott Endert, Alex |
| author_facet | Coscia, Adam Holmes, Langdon Morris, Wesley Choi, Joon Suh Crossley, Scott Endert, Alex |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_04760 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries Coscia, Adam Holmes, Langdon Morris, Wesley Choi, Joon Suh Crossley, Scott Endert, Alex Human-Computer Interaction Artificial Intelligence Computers and Society Machine Learning 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. |
| title | iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2403.04760 |