arXiv subjects

Stefan Larson

Publications and source records attributed to Stefan Larson.

18 recordsLinked to original sources

arXiv2026

Rebalancing Token Importance in Language Models with TF-IDF Weighted Cross-Entropy Loss

Large language models are typically trained under uniform token weighting, which allows frequent and low-information tokens to dominate learning and can increase the tendency to memorize surface-level text spans. To address this, we present an information-weighted cross-entropy loss that rescales token-level contributions using TF-IDF statistics, emphasizing semantically informative tokens while down-weighting ubiquitous ones. Experiments on five decoder-only LLMs ranging from 1.1B to 13B parameters show consistent reductions in memorized substring length while preserving perplexity and downstream task performance. Under LoRA fine-tuning, TF-IDF reduces average substring memorization length by 14% across all five models. Under full-weight fine-tuning on TinyLLaMA 1.1B, the reduction reaches 58%. Our approach is architecture-agnostic and can be incorporated into existing training pipelines with less than 3% computational overhead, offering a lightweight and principled way to mitigate memorization without disrupting standard training dynamics.

cs.CL↗

arXiv2026

Revising RVL-CDIP: Quantifying Errors and Test-Train Overlap

RVL-CDIP is a popular dataset for benchmarking document classifiers. However, the dataset contains ample amounts of label errors as well as non-trivial amounts of test-train overlap, both of which may impact model performance metrics. In this paper, we address these two problems by (1) finding and fixing label errors, and (2) detecting and addressing test-train overlap. We produce several variations of RVL-CDIP with label error and test-train overlap fixes, and benchmark document classification performance on these new RVL-CDIP variations. Our rigorous analysis of RVL-CDIP finds that the corpus contains 12\% label error and approximately 35% test-train duplication. Remediation sees improvements in classification accuracy when errors are removed, but sees decreases in accuracy when duplicates are removed. We additionally evaluate models on RVL-CDIP-N, an out-of-distribution benchmark, finding that training on error-corrected data substantially improves OOD generalization, with supervised models gaining an average of 8.1 percentage points in accuracy and improvements as large as 14 percentage points.

cs.CL↗

arXiv2024

Label Errors in the Tobacco3482 Dataset

Tobacco3482 is a widely used document classification benchmark dataset. However, our manual inspection of the entire dataset uncovers widespread ontological issues, especially large amounts of annotation label problems in the dataset. We establish data label guidelines and find that 11.7% of the dataset is improperly annotated and should either have an unknown label or a corrected label, and 16.7% of samples in the dataset have multiple valid labels. We then analyze the mistakes of a top-performing model and find that 35% of the model's mistakes can be directly attributed to these label issues, highlighting the inherent problems with using a noisily labeled dataset as a benchmark. Supplementary material, including dataset annotations and code, is available at https://github.com/gordon-lim/tobacco3482-mistakes/.

cs.CV↗

arXiv2024

Robust Testing for Deep Learning using Human Label Noise

In deep learning (DL) systems, label noise in training datasets often degrades model performance, as models may learn incorrect patterns from mislabeled data. The area of Learning with Noisy Labels (LNL) has introduced methods to effectively train DL models in the presence of noisily-labeled datasets. Traditionally, these methods are tested using synthetic label noise, where ground truth labels are randomly (and automatically) flipped. However, recent findings highlight that models perform substantially worse under human label noise than synthetic label noise, indicating a need for more realistic test scenarios that reflect noise introduced due to imperfect human labeling. This underscores the need for generating realistic noisy labels that simulate human label noise, enabling rigorous testing of deep neural networks without the need to collect new human-labeled datasets. To address this gap, we present Cluster-Based Noise (CBN), a method for generating feature-dependent noise that simulates human-like label noise. Using insights from our case study of label memorization in the CIFAR-10N dataset, we design CBN to create more realistic tests for evaluating LNL methods. Our experiments demonstrate that current LNL methods perform worse when tested using CBN, highlighting its use as a rigorous approach to testing neural networks. Next, we propose Soft Neighbor Label Sampling (SNLS), a method designed to handle CBN, demonstrating its improvement over existing techniques in tackling this more challenging type of noise.

cs.LG↗

arXiv2024

Towards Fair Pay and Equal Work: Imposing View Time Limits in Crowdsourced Image Classification

Crowdsourcing is a common approach to rapidly annotate large volumes of data in machine learning applications. Typically, crowd workers are compensated with a flat rate based on an estimated completion time to meet a target hourly wage. Unfortunately, prior work has shown that variability in completion times among crowd workers led to overpayment by 168% in one case, and underpayment by 16% in another. However, by setting a time limit for task completion, it is possible to manage the risk of overpaying or underpaying while still facilitating flat rate payments. In this paper, we present an analysis of the impact of a time limit on crowd worker performance and satisfaction. We conducted a human study with a maximum view time for a crowdsourced image classification task. We find that the impact on overall crowd worker performance diminishes as view time increases. Despite some images being challenging under time limits, a consensus algorithm remains effective at preserving data quality and filters images needing more time. Additionally, crowd workers' consistent performance throughout the time-limited task indicates sustained effort, and their psychometric questionnaire scores show they prefer shorter limits. Based on our findings, we recommend implementing task time limits as a practical approach to making compensation more equitable and predictable.

cs.HC↗

arXiv2024

Document Classification using File Names

Rapid document classification is critical in several time-sensitive applications like digital forensics and large-scale media classification. Traditional approaches that rely on heavy-duty deep learning models fall short due to high inference times over vast input datasets and computational resources associated with analyzing whole documents. In this paper, we present a method using lightweight supervised learning models, combined with a TF-IDF feature extraction-based tokenization method, to accurately and efficiently classify documents based solely on file names, that substantially reduces inference time. Our results indicate that file name classifiers can process more than 90% of in-scope documents with 99.63% and 96.57% accuracy when tested on two datasets, while being 442x faster than more complex models such as DiT. Our method offers a crucial solution to efficiently process vast document datasets in critical scenarios, enabling fast and more reliable document classification.

cs.CL↗

arXiv2024

Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification

Intent classifiers must be able to distinguish when a user's utterance does not belong to any supported intent to avoid producing incorrect and unrelated system responses. Although out-of-scope (OOS) detection for intent classifiers has been studied, previous work has not yet studied changes in classifier performance against hard-negative out-of-scope utterances (i.e., inputs that share common features with in-scope data, but are actually out-of-scope). We present an automated technique to generate hard-negative OOS data using ChatGPT. We use our technique to build five new hard-negative OOS datasets, and evaluate each against three benchmark intent classifiers. We show that classifiers struggle to correctly identify hard-negative OOS utterances more than general OOS utterances. Finally, we show that incorporating hard-negative OOS data for training improves model robustness when detecting hard-negative OOS data and general OOS data. Our technique, datasets, and evaluation address an important void in the field, offering a straightforward and inexpensive way to collect hard-negative OOS data and improve intent classifiers' robustness.

cs.CL↗

arXiv2023

On Evaluation of Document Classification using RVL-CDIP

The RVL-CDIP benchmark is widely used for measuring performance on the task of document classification. Despite its widespread use, we reveal several undesirable characteristics of the RVL-CDIP benchmark. These include (1) substantial amounts of label noise, which we estimate to be 8.1% (ranging between 1.6% to 16.9% per document category); (2) presence of many ambiguous or multi-label documents; (3) a large overlap between test and train splits, which can inflate model performance metrics; and (4) presence of sensitive personally-identifiable information like US Social Security numbers (SSNs). We argue that there is a risk in using RVL-CDIP for benchmarking document classifiers, as its limited scope, presence of errors (state-of-the-art models now achieve accuracy error rates that are within our estimated label error rate), and lack of diversity make it less than ideal for benchmarking. We further advocate for the creation of a new document classification benchmark, and provide recommendations for what characteristics such a resource should include.

cs.CL↗

arXiv2023

ShabbyPages: A Reproducible Document Denoising and Binarization Dataset

Document denoising and binarization are fundamental problems in the document processing space, but current datasets are often too small and lack sufficient complexity to effectively train and benchmark modern data-driven machine learning models. To fill this gap, we introduce ShabbyPages, a new document image dataset designed for training and benchmarking document denoisers and binarizers. ShabbyPages contains over 6,000 clean "born digital" images with synthetically-noised counterparts ("shabby pages") that were augmented using the Augraphy document augmentation tool to appear as if they have been printed and faxed, photocopied, or otherwise altered through physical processes. In this paper, we discuss the creation process of ShabbyPages and demonstrate the utility of ShabbyPages by training convolutional denoisers which remove real noise features with a high degree of human-perceptible fidelity, establishing baseline performance for a new ShabbyPages benchmark.

cs.CV↗

arXiv2022

Evaluating Out-of-Distribution Performance on Document Image Classifiers

The ability of a document classifier to handle inputs that are drawn from a distribution different from the training distribution is crucial for robust deployment and generalizability. The RVL-CDIP corpus is the de facto standard benchmark for document classification, yet to our knowledge all studies that use this corpus do not include evaluation on out-of-distribution documents. In this paper, we curate and release a new out-of-distribution benchmark for evaluating out-of-distribution performance for document classifiers. Our new out-of-distribution benchmark consists of two types of documents: those that are not part of any of the 16 in-domain RVL-CDIP categories (RVL-CDIP-O), and those that are one of the 16 in-domain categories yet are drawn from a distribution different from that of the original RVL-CDIP dataset (RVL-CDIP-N). While prior work on document classification for in-domain RVL-CDIP documents reports high accuracy scores, we find that these models exhibit accuracy drops of between roughly 15-30% on our new out-of-domain RVL-CDIP-N benchmark, and further struggle to distinguish between in-domain RVL-CDIP-N and out-of-domain RVL-CDIP-O inputs. Our new benchmark provides researchers with a valuable new resource for analyzing out-of-distribution performance on document classifiers. Our new out-of-distribution data can be found at https://github.com/gxlarson/rvl-cdip-ood.

cs.CV↗

arXiv2022

Augraphy: A Data Augmentation Library for Document Images

This paper introduces Augraphy, a Python library for constructing data augmentation pipelines which produce distortions commonly seen in real-world document image datasets. Augraphy stands apart from other data augmentation tools by providing many different strategies to produce augmented versions of clean document images that appear as if they have been altered by standard office operations, such as printing, scanning, and faxing through old or dirty machines, degradation of ink over time, and handwritten markings. This paper discusses the Augraphy tool, and shows how it can be used both as a data augmentation tool for producing diverse training data for tasks such as document denoising, and also for generating challenging test data to evaluate model robustness on document image modeling tasks.

cs.CV↗

arXiv2022

K-ASTRO: Structure-Aware Adaptation of LLMs for Code Vulnerability Detection

Large Language Models (LLMs) are transforming software engineering tasks, including code vulnerability detection-a critical area of software security. However, existing methods often rely on resource-intensive models or graph-based techniques, limiting their accessibility and practicality. This paper introduces K-ASTRO, a lightweight Transformer model that combines semantic embeddings from LLMs with structural features of Abstract Syntax Trees (ASTs) to improve both efficiency and accuracy in code vulnerability detection. Our approach introduces an AST-based augmentation technique inspired by mutation testing, a structure-aware attention mechanism that incorporates augmented AST features, and a joint adaptation pipeline to unify code semantics and syntax. Experimental results on three large-scale datasets, including BigVul, DiverseVul, and PrimeVul-demonstrate state-of-the-art performance while enabling rapid inference on CPUs with minimal training time. By offering a scalable, interpretable, and efficient solution, K-ASTRO bridges the gap between LLM advancements and practical software vulnerability detection, providing open-sourced tools to foster further research.

cs.SE↗

arXiv2022

A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog

Interest in dialog systems has grown substantially in the past decade. By extension, so too has interest in developing and improving intent classification and slot-filling models, which are two components that are commonly used in task-oriented dialog systems. Moreover, good evaluation benchmarks are important in helping to compare and analyze systems that incorporate such models. Unfortunately, much of the literature in the field is limited to analysis of relatively few benchmark datasets. In an effort to promote more robust analyses of task-oriented dialog systems, we have conducted a survey of publicly available datasets for the tasks of intent classification and slot-filling. We catalog the important characteristics of each dataset, and offer discussion on the applicability, strengths, and weaknesses of each. The emergence of large language models (LLMs) as capable zero-shot NLU systems gives such benchmarks a new role: the corpora cataloged here provide the principled evaluation infrastructure needed to measure LLM capabilities in structured NLU tasks, compare them against specialized models, and identify the settings---multilingual, multi-intent, low-resource---where significant gaps remain. Our goal is that this survey aids in increasing the accessibility of these datasets, which we hope will enable their use in future evaluations of intent classification and slot-filling models, whether those models are task-specific classifiers, fine-tuned language models, or zero-shot LLMs.

cs.CL↗

arXiv2022

Redwood: Using Collision Detection to Grow a Large-Scale Intent Classification Dataset

Dialog systems must be capable of incorporating new skills via updates over time in order to reflect new use cases or deployment scenarios. Similarly, developers of such ML-driven systems need to be able to add new training data to an already-existing dataset to support these new skills. In intent classification systems, problems can arise if training data for a new skill's intent overlaps semantically with an already-existing intent. We call such cases collisions. This paper introduces the task of intent collision detection between multiple datasets for the purposes of growing a system's skillset. We introduce several methods for detecting collisions, and evaluate our methods on real datasets that exhibit collisions. To highlight the need for intent collision detection, we show that model performance suffers if new data is added in such a way that does not arbitrate colliding intents. Finally, we use collision detection to construct and benchmark a new dataset, Redwood, which is composed of 451 ntent categories from 13 original intent classification datasets, making it the largest publicly available intent classification benchmark.

cs.CL↗

arXiv2021

Exploring Out-of-Distribution Generalization in Text Classifiers Trained on Tobacco-3482 and RVL-CDIP

To be robust enough for widespread adoption, document analysis systems involving machine learning models must be able to respond correctly to inputs that fall outside of the data distribution that was used to generate the data on which the models were trained. This paper explores the ability of text classifiers trained on standard document classification datasets to generalize to out-of-distribution documents at inference time. We take the Tobacco-3482 and RVL-CDIP datasets as a starting point and generate new out-of-distribution evaluation datasets in order to analyze the generalization performance of models trained on these standard datasets. We find that models trained on the smaller Tobacco-3482 dataset perform poorly on our new out-of-distribution data, while text classification models trained on the larger RVL-CDIP exhibit smaller performance drops.

cs.CL↗

arXiv2021

LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction

Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailment, and natural language understanding. However, current OIE datasets are limited in both size and diversity. We introduce a new dataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset (LSOIE). Our LSOIE dataset is 20 times larger than the next largest human-annotated OIE dataset. We construct and evaluate several benchmark OIE models on LSOIE, providing baselines for future improvements on the task. Our LSOIE data, models, and code are made publicly available

cs.CL↗

arXiv2019

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope---i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.

cs.CL↗

arXiv2019

Outlier Detection for Improved Data Quality and Diversity in Dialog Systems

In a corpus of data, outliers are either errors: mistakes in the data that are counterproductive, or are unique: informative samples that improve model robustness. Identifying outliers can lead to better datasets by (1) removing noise in datasets and (2) guiding collection of additional data to fill gaps. However, the problem of detecting both outlier types has received relatively little attention in NLP, particularly for dialog systems. We introduce a simple and effective technique for detecting both erroneous and unique samples in a corpus of short texts using neural sentence embeddings combined with distance-based outlier detection. We also present a novel data collection pipeline built atop our detection technique to automatically and iteratively mine unique data samples while discarding erroneous samples. Experiments show that our outlier detection technique is effective at finding errors while our data collection pipeline yields highly diverse corpora that in turn produce more robust intent classification and slot-filling models.

cs.CL↗