# Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models

arXiv: https://arxiv.org/abs/2310.02161 · HTML: https://mlsanity.com/paper/2310.02161/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 126 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA](https://mlsanity.com/paper/2109.05014/) (2021)
- [A Review on Language Models as Knowledge Bases](https://mlsanity.com/paper/2204.06031/) (2022)
- [Language Models are Open Knowledge Graphs](https://mlsanity.com/paper/2010.11967/) (2020)
- [Crawling the Internal Knowledge-Base of Language Models](https://mlsanity.com/paper/2301.12810/) (2023)
- [Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://mlsanity.com/paper/2210.07700/) (2022)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- ["What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models](https://mlsanity.com/paper/2304.06597/) (2023)
- [Scim: Intelligent Skimming Support for Scientific Papers](https://mlsanity.com/paper/2205.04561/) (2022)
- [Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing](https://mlsanity.com/paper/2203.00130/) (2022)
- [Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature](https://mlsanity.com/paper/2208.03455/) (2022)
- [Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Making](https://mlsanity.com/paper/2202.02175/) (2022)
- [Fuse: In-Situ Sensemaking Support in the Browser](https://mlsanity.com/paper/2208.14861/) (2022)
- [To Reuse or Not To Reuse? A Framework and System for Evaluating Summarized Knowledge](https://mlsanity.com/paper/2102.06231/) (2021)
- [Wigglite: Low-cost Information Collection and Triage](https://mlsanity.com/paper/2208.00496/) (2022)
- [How does AI chat change search behaviors?](https://mlsanity.com/paper/2307.03826/) (2023)
- [Co-audit: tools to help humans double-check AI-generated content](https://mlsanity.com/paper/2310.01297/) (2023)
- [Understanding How Programmers Can Use Annotations on Documentation](https://mlsanity.com/paper/2111.08684/) (2021)
- [SUMMIT: Scaffolding OSS Issue Discussion Through Summarization](https://mlsanity.com/paper/2308.02780/) (2023)

## Built on by (most cited first, 10 of 10)

- [Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows](https://mlsanity.com/paper/2502.18737/) (2025)
- [Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale](https://mlsanity.com/paper/2504.09283/) (2025)
- [Evalet: Evaluating Large Language Models through Functional Fragmentation](https://mlsanity.com/paper/2509.11206/) (2025)
- [Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning](https://mlsanity.com/paper/2501.15727/) (2025)
- [ClimateSOM: A Visual Analysis Workflow for Climate Ensemble Datasets](https://mlsanity.com/paper/2508.06732/) (2025)
- [Orchestrating Attention: Bringing Harmony to the 'Chaos' of Neurodivergent Learning States](https://mlsanity.com/paper/2602.07865/) (2026)
- [LitLinker: Supporting the Ideation of Interdisciplinary Contexts with Large Language Models for Teaching Literature in Elementary Schools](https://mlsanity.com/paper/2502.16097/) (2025)
- [Exploring the Innovation Opportunities for Pre-trained Models](https://mlsanity.com/paper/2505.15790/) (2025)
- [IntentWeave: A Progressive Entry Ladder for Multi-Surface Browser Agents in Cloud Portals](https://mlsanity.com/paper/2603.22917/) (2026)
- [MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration](https://mlsanity.com/paper/2604.23129/) (2026)

## Abstract (arXiv)

Sensemaking in unfamiliar domains can be challenging, demanding considerable user effort to compare different options with respect to various criteria. Prior research and our formative study found that people would benefit from reading an overview of an information space upfront, including the criteria others previously found useful. However, existing sensemaking tools struggle with the "cold-start" problem -- it not only requires significant input from previous users to generate and share these overviews, but such overviews may also turn out to be biased and incomplete. In this work, we introduce a novel system, Selenite, which leverages Large Language Models (LLMs) as reasoning machines and knowledge retrievers to automatically produce a comprehensive overview of options and criteria to jumpstart users' sensemaking processes. Subsequently, Selenite also adapts as people use it, helping users find, read, and navigate unfamiliar information in a systematic yet personalized manner. Through three studies, we found that Selenite produced accurate and high-quality overviews reliably, significantly accelerated users' information processing, and effectively improved their overall comprehension and sensemaking experience.