Publications (92)
MATCHA: Matching Text via Contrastive Semantic Alignment
Siran Li, Ece Sena Etoglu, Carsten Eickhoff +1
Reliable evaluation is essential for understanding large language model (LLM) performance, yet today's go-to metrics, namely token-overlap scores (e.g., ROUGE) and embedding-based…
Pretraining on Interactions for Learning Grounded Affordance Representations
Jack Merullo, Dylan Ebert, Carsten Eickhoff +1
Lexical semantics and cognitive science point to affordances (i.e. the actions that objects support) as critical for understanding and representing nouns and verbs. However, study…
TRIM: Achieving Extreme Sparsity with Targeted Row-wise Iterative Metric-driven Pruning
Florentin Beck, William Rudman, Carsten Eickhoff
Large Language Models (LLMs) present significant computational and memory challenges due to their extensive size, making pruning essential for their efficient deployment. Existing…
A Mechanistic Analysis of Gender Sensitivity in Dense Retrieval Models
Catherine Chen, Maarten de Rijke, Carsten Eickhoff
While gender bias in dense retrieval models is well documented, with prior work showing that models often score male-gendered documents higher than female or neutral variants, the…
Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance
Reza Esfandiarpoor, George Zerveas, Ruochen Zhang +3
Although synthetic data has changed various aspects of information retrieval (IR) pipelines, the main training paradigm remains: contrastive learning with binary relevance labels,…
DC3 -- A Diagnostic Case Challenge Collection for Clinical Decision Support
Carsten Eickhoff, Floran Gmehlin, Anu V. Patel +2
In clinical care, obtaining a correct diagnosis is the first step towards successful treatment and, ultimately, recovery. Depending on the complexity of the case, the diagnostic ph…
One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data
Michal Golovanevsky, Eva Schiller, Akira Nair +3
Multimodal learning models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to autonomous driving. Des…
Garden-Path Traversal in GPT-2
William Jurayj, William Rudman, Carsten Eickhoff
In recent years, large-scale transformer decoders such as the GPT-x family of models have become increasingly popular. Studies examining the behavior of these models tend to focus…
Computing Web-scale Topic Models using an Asynchronous Parameter Server
Rolf Jagerman, Carsten Eickhoff, Maarten de Rijke
Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory…
Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models
Catherine Chen, Jack Merullo, Carsten Eickhoff
Neural models have demonstrated remarkable performance across diverse ranking tasks. However, the processes and internal mechanisms along which they determine relevance are still l…
CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization
Ruochen Zhang, Carsten Eickhoff
Cross-lingual summarization (CLS) has attracted increasing interest in recent years due to the availability of large-scale web-mined datasets and the advancements of multilingual l…
Outlier Dimensions Encode Task-Specific Knowledge
William Rudman, Catherine Chen, Carsten Eickhoff
Representations from large language models (LLMs) are known to be dominated by a small subset of dimensions with exceedingly high variance. Previous works have argued that although…
IsoScore: Measuring the Uniformity of Embedding Space Utilization
William Rudman, Nate Gillman, Taylor Rayne +1
The recent success of distributed word representations has led to an increased interest in analyzing the properties of their spatial distribution. Several studies have suggested th…
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors
George Zerveas, Navid Rekabsaz, Carsten Eickhoff
Sparse annotation poses persistent challenges to training dense retrieval models; for example, it distorts the training signal when unlabeled relevant documents are used spuriously…
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
Daniel Cohen, Bhaskar Mitra, Oleg Lesota +2
In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query. While retrieval models have cont…
Efficient Parallel Learning of Word2Vec
Jeroen B. P. Vuurens, Carsten Eickhoff, Arjen P. de Vries
Since its introduction, Word2Vec and its variants are widely used to learn semantics-preserving representations of words or entities in an embedding space, which can be used to pro…
Semantic Place Descriptors for Classification and Map Discovery
Siddharth Sarda, Carsten Eickhoff, Thomas Hofmann
Urban environments develop complex, non-obvious structures that are often hard to represent in the form of maps or guides. Finding the right place to go often requires intimate fam…
Stable Anisotropic Regularization
William Rudman, Carsten Eickhoff
Given the success of Large Language Models (LLMs), there has been considerable interest in studying the properties of model activations. The literature overwhelmingly agrees that L…
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Oleg Lesota, Navid Rekabsaz, Daniel Cohen +3
Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich litera…
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking
George Zerveas, Navid Rekabsaz, Daniel Cohen +1
Contrastive learning has been the dominant approach to training dense retrieval models. In this work, we investigate the impact of ranking context - an often overlooked aspect of l…
Beyond Multiple Choice: Evaluating Steering Vectors for Summarization
Joschka Braun, Carsten Eickhoff, Seyed Ali Bahrainian
Steering vectors are a lightweight method for controlling text properties by adding a learned bias to language model activations at inference time. While predominantly studied for…
Forgotten Polygons: Multimodal Large Language Models are Shape-Blind
William Rudman, Michal Golovanevsky, Amir Bar +4
Despite strong performance on vision-language tasks, Multimodal Large Language Models (MLLMs) struggle with mathematical problem-solving, with both open-source and state-of-the-art…
PiCME: Pipeline for Contrastive Modality Evaluation and Encoding in the MIMIC Dataset
Michal Golovanevsky, Pranav Mahableshwarkar, Carsten Eickhoff +1
Multimodal deep learning holds promise for improving clinical prediction by integrating diverse patient data, including text, imaging, time-series, and structured demographics. Con…
Web2Text: Deep Structured Boilerplate Removal
Thijs Vogels, Octavian-Eugen Ganea, Carsten Eickhoff
Web pages are a valuable source of information for many natural language processing and information retrieval tasks. Extracting the main content from those documents is essential f…
Vision-Default, Prior-Override: Causal Mechanisms of Perception-Knowledge Conflict in Vision-Language Models
Niclas Lietzow, Danielle Bitterman, Carsten Eickhoff +2
Vision-language models must reconcile visual evidence with memorized world knowledge when the two conflict. How they resolve this conflict shapes the reliability of multimodal syst…
On the Effect of Low-Frequency Terms on Neural-IR Models
Sebastian Hofstätter, Navid Rekabsaz, Carsten Eickhoff +1
Low-frequency terms are a recurring challenge for information retrieval models, especially neural IR frameworks struggle with adequately capturing infrequently observed words. Whil…
Circuit Component Reuse Across Tasks in Transformer Language Models
Jack Merullo, Carsten Eickhoff, Ellie Pavlick
Recent work in mechanistic interpretability has shown that behaviors in language models can be successfully reverse-engineered through circuit analysis. A common criticism, however…
Evaluating Music Recommender Systems for Groups
Zsolt Mezei, Carsten Eickhoff
Recommendation to groups of users is a challenging and currently only passingly studied task. Especially the evaluation aspect often appears ad-hoc and instead of truly evaluating…
Benchmarking is Broken -- Don't Let AI be its Own Judge
Zerui Cheng, Stella Wohnig, Ruchika Gupta +13
The meteoric rise of AI, with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need…
Impact Factors for Computer Science Conferences
Carsten Eickhoff
An increasing number of CS researchers are employed in academic non-CS departments where publication output is measured in terms of journal impact factors. To foster recognition of…
Predictive Uncertainty-based Bias Mitigation in Ranking
Maria Heuss, Daniel Cohen, Masoud Mansoury +2
Societal biases that are contained in retrieved documents have received increased interest. Such biases, which are often prevalent in the training data and learned by the model, ca…
Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline
Meng Lu, Ruochen Zhang, Carsten Eickhoff +1
Multilingual large language models (LLMs) often exhibit factual inconsistencies across languages, with significantly better performance in factual recall tasks in English than in o…
Image-Like Graph Representations for Improved Molecular Property Prediction
Toni Sagayaraj, Carsten Eickhoff
Research into deep learning models for molecular property prediction has primarily focused on the development of better Graph Neural Network (GNN) architectures. Though new GNN var…
Understanding Wacky Weights: A Dissection of SPLADE's Learned Term Importance
Gregory Polyakov, Harrisen Scells, Carsten Eickhoff
Learned sparse retrieval models such as SPLADE combine the effectiveness of neural architectures with the efficiency of inverted indices. As these models assign weights to terms fr…
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking
Octavian-Eugen Ganea, Marina Ganea, Aurelien Lucchi +2
Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental…
A Cross-Platform Collection of Social Network Profiles
Maria Han Veiga, Carsten Eickhoff
The proliferation of Internet-enabled devices and services has led to a shifting balance between digital and analogue aspects of our everyday lives. In the face of this development…
Enhancing Retrieval-Augmented Generation: A Study of Best Practices
Siran Li, Linus Stenzel, Carsten Eickhoff +1
Retrieval-Augmented Generation (RAG) systems have recently shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produc…
The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling
Ruochen Zhang, Qinan Yu, Matianyu Zang +2
We employ new tools from mechanistic interpretability in order to ask whether the internal structure of large language models (LLMs) shows correspondence to the linguistic structur…
Language Models Implement Simple Word2Vec-style Vector Arithmetic
Jack Merullo, Carsten Eickhoff, Ellie Pavlick
A primary criticism towards language models (LMs) is their inscrutability. This paper presents evidence that, despite their size and complexity, LMs sometimes exploit a simple vect…
Neural Document Embeddings for Intensive Care Patient Mortality Prediction
Paulina Grnarova, Florian Schmidt, Stephanie L. Hyland +1
We present an automatic mortality prediction scheme based on the unstructured textual content of clinical notes. Proposing a convolutional document embedding approach, our empirica…
UbuntuGuard: A Culturally-Grounded Policy Benchmark for Equitable AI Safety in African Languages
Tassallah Abdullahi, Macton Mgonzo, Mardiyyah Oduwole +4
Current guardian models are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerable to evolving harms, cross-ling…
Linearly Mapping from Image to Text Space
Jack Merullo, Louis Castricato, Carsten Eickhoff +1
The extent to which text-only language models (LMs) learn to represent features of the non-linguistic world is an open question. Prior work has shown that pretrained LMs can be tau…
Biomedical Question Answering via Weighted Neural Network Passage Retrieval
Ferenc Galkó, Carsten Eickhoff
The amount of publicly available biomedical literature has been growing rapidly in recent years, yet question answering systems still struggle to exploit the full potential of this…
APP: Accelerated Path Patching with Task-Specific Pruning
Frauke Andersen, William Rudman, Ruochen Zhang +1
Circuit discovery is a key step in many mechanistic interpretability pipelines. Current methods, such as Path Patching, are computationally expensive and have limited in-depth circ…
SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain
Ruochen Zhang, Carsten Eickhoff
In the pursuit of natural language understanding, there has been a long standing interest in tracking state changes throughout narratives. Impressive progress has been made in mode…
Pathway to Relevance: How Cross-Encoders Implement a Semantic Variant of BM25
Meng Lu, Catherine Chen, Carsten Eickhoff
Mechanistic interpretation has greatly contributed to a more detailed understanding of generative language models, enabling significant progress in identifying structures that impl…
Understanding (Un)Reliability of Steering Vectors in Language Models
Joschka Braun, Carsten Eickhoff, David Krueger +2
Steering vectors are a lightweight method to control language model behavior by adding a learned bias to the activations at inference time. Although steering demonstrates promising…
What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Gaussian-Noise-free Text-Image Corruption and Evaluation
Michal Golovanevsky, William Rudman, Vedant Palit +2
Vision-Language Models (VLMs) have gained community-spanning prominence due to their ability to integrate visual and textual inputs to perform complex tasks. Despite their success,…
Retrieval Augmented Zero-Shot Text Classification
Tassallah Abdullahi, Ritambhara Singh, Carsten Eickhoff
Zero-shot text learning enables text classifiers to handle unseen classes efficiently, alleviating the need for task-specific training data. A simple approach often relies on compa…
A Novel Corpus of Discourse Structure in Humans and Computers
Babak Hemmatian, Sheridan Feucht, Rachel Avram +7
We present a novel corpus of 445 human- and computer-generated documents, comprising about 27,000 clauses, annotated for semantic clause types and coherence relations that allow fo…
Is There Knowledge Left to Extract? Evidence of Fragility in Medically Fine-Tuned Vision-Language Models
Oliver McLaughlin, Daniel Shubin, Carsten Eickhoff +3
Vision-language models (VLMs) are increasingly adapted through domain-specific fine-tuning, yet it remains unclear whether this improves reasoning beyond superficial visual cues, p…
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG
İlkay Yıldız Potter, George Zerveas, Carsten Eickhoff +1
Epilepsy is one of the most common neurological disorders, typically observed via seizure episodes. Epileptic seizures are commonly monitored through electroencephalogram (EEG) rec…
Crosslingual Reasoning through Test-Time Scaling
Zheng-Xin Yong, M. Farid Adilazuarda, Jonibek Mansurov +7
Reasoning capabilities of large language models are primarily studied for English, even when pretrained models are multilingual. In this work, we investigate to what extent English…
Mechanisms of Prompt-Induced Hallucination in Vision-Language Models
William Rudman, Michal Golovanevsky, Dana Arad +4
Large vision-language models (VLMs) are highly capable, yet often hallucinate by favoring textual prompts over visual evidence. We study this failure mode in a controlled object-co…
Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis
Michal Golovanevsky, Carsten Eickhoff, Ritambhara Singh
Alzheimer's Disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support diffic…
MechIR: A Mechanistic Interpretability Framework for Information Retrieval
Andrew Parry, Catherine Chen, Carsten Eickhoff +1
Mechanistic interpretability is an emerging diagnostic approach for neural models that has gained traction in broader natural language processing domains. This paradigm aims to pro…
TripClick: The Log Files of a Large Health Web Search Engine
Navid Rekabsaz, Oleg Lesota, Markus Schedl +2
Click logs are valuable resources for a variety of information retrieval (IR) tasks. This includes query understanding/analysis, as well as learning effective IR models particularl…
ExpertRank: A Multi-level Coarse-grained Expert-based Listwise Ranking Loss
Zhizhong Chen, Carsten Eickhoff
The goal of information retrieval is to recommend a list of document candidates that are most relevant to a given query. Listwise learning trains neural retrieval models by compari…
Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language Models through Visual Counterfacts
Michal Golovanevsky, William Rudman, Michael Lepori +3
Multimodal Large Language Models (MLLMs) perform well on tasks such as visual question answering, but it remains unclear whether their reasoning relies more on memorized world know…
Diagnosis Prevalence vs. Efficacy in Machine-learning Based Diagnostic Decision Support
Gil Alon, Elizabeth Chen, Guergana Savova +1
Many recent studies use machine learning to predict a small number of ICD-9-CM codes. In practice, on the other hand, physicians have to consider a broader range of diagnoses. This…
When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?
Xinyu Zhou, Chang Jin, Carsten Eickhoff +2
Large language models (LLMs) rarely admit uncertainty, often producing fluent but misleading answers, rather than abstaining (i.e., refusing to answer). This weakness is even evide…
PoolRank: Max/Min Pooling-based Ranking Loss for Listwise Learning & Ranking Balance
Zhizhong Chen, Carsten Eickhoff
Numerous neural retrieval models have been proposed in recent years. These models learn to compute a ranking score between the given query and document. The majority of existing mo…
Neural Summarization of Electronic Health Records
Koyena Pal, Seyed Ali Bahrainian, Laura Mercurio +1
Hospital discharge documentation is among the most essential, yet time-consuming documents written by medical practitioners. The objective of this study was to automatically genera…
Isotropy Cliffs: The Geometric Signature of Decision-Making in Large Language Models
Okan S. Coskun, Florian Rottach, Carsten Eickhoff +1
We investigate the geometry of decision-making in Multiple Choice Question Answering (MCQA) through the lens of isotropy. Analyzing five open-weight models across diverse datasets,…
Controllable Topic-Focused Abstractive Summarization
Seyed Ali Bahrainian, Martin Jaggi, Carsten Eickhoff
Controlled abstractive summarization focuses on producing condensed versions of a source article to cover specific aspects by shifting the distribution of generated text towards a…
A Survey on LLM-Assisted Clinical Trial Recruitment
Shrestha Ghosh, Moritz Schneider, Carina Reinicke +1
Recent advances in LLMs have greatly improved general-domain NLP tasks. Yet, their adoption in critical domains, such as clinical trial recruitment, remains limited. As trials are…
Evaluating Search System Explainability with Psychometrics and Crowdsourcing
Catherine Chen, Carsten Eickhoff
As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grow…
PubMed-Ophtha: An open resource for training ophthalmology vision-language models on scientific literature
Verena Jasmin Hallitschke, Carsten Eickhoff, Philipp Berens
Vision-language models hold considerable promise for ophthalmology, but their development depends on large-scale, high-quality image-text datasets that remain scarce. We present Pu…
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Deepak Kumar, Oleg Lesota, George Zerveas +4
Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial train…
Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings
Yuanfei Dai, Chenhao Guo, Wenzhong Guo +1
Interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDI) is one of t…
A Transformer-based Framework for Multivariate Time Series Representation Learning
George Zerveas, Srideepika Jayaraman, Dhaval Patel +2
In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series. Pre-trained models can be potentially…
Brown University at TREC Deep Learning 2019
George Zerveas, Ruochen Zhang, Leila Kim +1
This paper describes Brown University's submission to the TREC 2019 Deep Learning track. We followed a 2-phase method for producing a ranking of passages for a given input query: I…
NEWTS: A Corpus for News Topic-Focused Summarization
Seyed Ali Bahrainian, Sheridan Feucht, Carsten Eickhoff
Text summarization models are approaching human levels of fidelity. Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or, profession…
Privacy Leakage through Innocent Content Sharing in Online Social Networks
Maria Han Veiga, Carsten Eickhoff
The increased popularity and ubiquitous availability of online social networks and globalised Internet access have affected the way in which people share content. The information t…
Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification
Abdullah Ahmed, Adeel Abbasi, Carsten Eickhoff
Electronic Health Records (EHRs) have become the primary form of medical data-keeping across the United States. Federal law restricts the sharing of any EHR data that contains prot…
Mining Misdiagnosis Patterns from Biomedical Literature
Cindy Li, Elizabeth Chen, Guergana Savova +2
Diagnostic errors can pose a serious threat to patient safety, leading to serious harm and even death. Efforts are being made to develop interventions that allow physicians to reas…
Less is More: Label-Guided Summarization of Procedural and Instructional Videos
Shreya Rajpal, Michal Golovanevsky, Carsten Eickhoff
Video summarization helps turn long videos into clear, concise representations that are easier to review, document, and analyze, especially in high-stakes domains like surgical tra…
The Persona Paradox: Medical Personas as Behavioral Priors in Clinical Language Models
Tassallah Abdullahi, Shrestha Ghosh, Hamish S Fraser +5
Persona conditioning can be viewed as a behavioral prior for large language models (LLMs) and is often assumed to confer expertise and improve safety in a monotonic manner. However…
When to Call an Apple Red: Humans Follow Introspective Rules, VLMs Don't
Jonathan Nemitz, Carsten Eickhoff, Junyi Jessy Li +3
Understanding when Vision-Language Models (VLMs) will behave unexpectedly, whether models can reliably predict their own behavior, and if models adhere to their introspective reaso…
From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures
Florian Rottach, William Rudman, Bastian Rieck +2
Studying how embeddings are organized in space not only enhances model interpretability but also uncovers factors that drive downstream task performance. In this paper, we present…
K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction
Tassallah Abdullahi, Ioanna Gemou, Nihal V. Nayak +4
Biomedical knowledge graphs (KGs) encode rich, structured information critical for drug discovery tasks, but extracting meaningful insights from large-scale KGs remains challenging…
Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling
Jing Yang, Carsten Eickhoff
Many social network applications depend on robust representations of spatio-temporal data. In this work, we present an embedding model based on feed-forward neural networks which t…
SSE: A Metric for Evaluating Search System Explainability
Catherine Chen, Carsten Eickhoff
Explainable Information Retrieval (XIR) is a growing research area focused on enhancing transparency and trustworthiness of the complex decision-making processes taking place in mo…
Talking Heads: Understanding Inter-layer Communication in Transformer Language Models
Jack Merullo, Carsten Eickhoff, Ellie Pavlick
Although it is known that transformer language models (LMs) pass features from early layers to later layers, it is not well understood how this information is represented and route…
Beyond One-Time Validation: A Framework for Adaptive Validation of Prognostic and Diagnostic AI-based Medical Devices
Florian Hellmeier, Kay Brosien, Carsten Eickhoff +1
Prognostic and diagnostic AI-based medical devices hold immense promise for advancing healthcare, yet their rapid development has outpaced the establishment of appropriate validati…
When BERT Fails -- The Limits of EHR Classification
Augusto Garcia-Agundez, Carsten Eickhoff
Transformers are powerful text representation learners, useful for all kinds of clinical decision support tasks. Although they outperform baselines on readmission prediction, they…
Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding
Yuanfei Dai, Wenzhong Guo, Carsten Eickhoff
Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of…
Implicit Negative Feedback in Clinical Information Retrieval
Lorenz Kuhn, Carsten Eickhoff
In this paper, we reflect on ways to improve the quality of bio-medical information retrieval by drawing implicit negative feedback from negated information in noisy natural langua…
The Cross-Lingual Arabic Information REtrieval (CLAIRE) System
Zhizhong Chen, Carsten Eickhoff
Despite advances in neural machine translation, cross-lingual retrieval tasks in which queries and documents live in different natural language spaces remain challenging. Although…
Embedding Electronic Health Records for Clinical Information Retrieval
Xing Wei, Carsten Eickhoff
Neural network representation learning frameworks have recently shown to be highly effective at a wide range of tasks ranging from radiography interpretation via data-driven diagno…
Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector Spaces
Albert Webson, Zhizhong Chen, Carsten Eickhoff +1
In politics, neologisms are frequently invented for partisan objectives. For example, "undocumented workers" and "illegal aliens" refer to the same group of people (i.e., they have…
Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures
Aaron S. Eisman, Nishant R. Shah, Carsten Eickhoff +4
Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular management. This study evaluated the potential to extract these symptoms from physician…