Deletion Considered Harmful
- URL: http://arxiv.org/abs/2512.23907v1
- Date: Tue, 30 Dec 2025 00:08:32 GMT
- Title: Deletion Considered Harmful
- Authors: Paul Englefield, Russell Beale,
- Abstract summary: We set out to understand how people view deletion overload and the removal of material no longer needed.<n>We studied the behaviour of 51 knowledge workers to evaluate a range of tactics they used aimed at organizing, filing, and retrieving digital resources.<n>Our study reveals that deletion is consistently under-adopted compared to other tactics such as Filing, Coverage, Ontology, and Timeliness.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In a world of information overload, understanding how we can most effectively manage information is crucial to success. We set out to understand how people view deletion, the removal of material no longer needed: does it help by reducing clutter and improving the signal to noise ratio, or does the effort required to decide to delete something make it not worthwhile? How does deletion relate to other strategies like filing; do people who spend extensive time in filing also prune their materials too? We studied the behaviour of 51 knowledge workers though a series of questionnaires and interviews to evaluate a range of tactics they used aimed at organizing, filing, and retrieving digital resources. Our study reveals that deletion is consistently under-adopted compared to other tactics such as Filing, Coverage, Ontology, and Timeliness. Moreover, the empirical data indicate that deletion is actually detrimental to retrieval success and satisfaction. In this paper, we examine the practice of deletion, review the related literature, and present detailed statistical results and clustering outcomes that underscore its adverse effects.
Related papers
- LLM Unlearning on Noisy Forget Sets: A Study of Incomplete, Rewritten, and Watermarked Data [69.5099112089508]
Large language models (LLMs) exhibit remarkable generative capabilities but raise ethical and security concerns by memorizing sensitive data.<n>This work presents the first study of unlearning under perturbed or low-fidelity forget data, referred to as noisy forget sets.<n>We find that unlearning remains surprisingly robust to perturbations, provided that core semantic signals are preserved.
arXiv Detail & Related papers (2025-10-10T05:10:49Z) - Understanding the Dilemma of Unlearning for Large Language Models [50.54260066313032]
Unlearning seeks to remove specific knowledge from large language models (LLMs)<n>We propose unPact, an interpretable framework for unlearning via prompt attribution and contribution tracking.
arXiv Detail & Related papers (2025-09-29T12:15:19Z) - SoK: Machine Unlearning for Large Language Models [14.88062383081161]
Large language model (LLM) unlearning has become a critical topic in machine learning.<n>We propose a new taxonomy based on the intention of unlearning.
arXiv Detail & Related papers (2025-06-10T20:30:39Z) - Extracting Unlearned Information from LLMs with Activation Steering [46.16882599881247]
Unlearning has emerged as a solution to remove sensitive knowledge from models after training.
We propose activation steering as a method for exact information retrieval from unlearned models.
Our results demonstrate that exact information retrieval from unlearned models is possible, highlighting a severe vulnerability of current unlearning techniques.
arXiv Detail & Related papers (2024-11-04T21:42:56Z) - Data Deletion for Linear Regression with Noisy SGD [9.784347635082232]
We present the perfect deleted point problem for 1-step noisy SGD in the classical linear regression task.
This research underscores the importance of data deletion and calls for urgent need for more studies in this field.
arXiv Detail & Related papers (2024-10-12T00:20:26Z) - MUSE: Machine Unlearning Six-Way Evaluation for Language Models [109.76505405962783]
Language models (LMs) are trained on vast amounts of text data, which may include private and copyrighted content.
We propose MUSE, a comprehensive machine unlearning evaluation benchmark.
We benchmark how effectively eight popular unlearning algorithms can unlearn Harry Potter books and news articles.
arXiv Detail & Related papers (2024-07-08T23:47:29Z) - The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
Large Language Models (LLMs) are foundational to AI advancements.
LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information.
Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
arXiv Detail & Related papers (2024-03-23T09:26:15Z) - Rethinking Negative Pairs in Code Search [56.23857828689406]
We propose a simple yet effective Soft-InfoNCE loss that inserts weight terms into InfoNCE.
We analyze the effects of Soft-InfoNCE on controlling the distribution of learnt code representations and on deducing a more precise mutual information estimation.
arXiv Detail & Related papers (2023-10-12T06:32:42Z) - ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information.<n>To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles.<n>Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
arXiv Detail & Related papers (2023-05-23T16:40:07Z) - Evaluating Inexact Unlearning Requires Revisiting Forgetting [14.199668091405064]
We introduce a novel test to measure the degree of forgetting called Interclass Confusion (IC)
Despite being a black-box test, IC can investigate whether information from the deletion set was erased until the early layers of the network.
We empirically show that two simple unlearning methods, exact-unlearning and catastrophic-forgetting the final k layers of a network, scale well to large deletion sets unlike prior unlearning methods.
arXiv Detail & Related papers (2022-01-17T21:49:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.