Search Results for author:
Found 40 papers, 18 papers with code
Identifying Distributional Perspectives from Colingual Groups
Discrepancies exist among different cultures or languages.
Offset Unlearning for Large Language Models
Despite the strong capabilities of Large Language Models (LLMs) to acquire knowledge from their training corpora, the memorization of sensitive information in the corpora such as copyrighted, harmful, and private content has led to ethical and legal concerns.
Secret Keepers: The Impact of LLMs on Linguistic Markers of Personal Traits
However, with the increasing adoption of Large Language Models (LLMs) as writing assistants in everyday writing, a critical question emerges: are authors' linguistic patterns still predictive of their personal traits when LLMs are involved in the writing process?
Risk and Response in Large Language Models: Evaluating Key Threat Categories
This paper explores the pressing issue of risk assessment in Large Language Models (LLMs) as they become increasingly prevalent in various applications.
Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations
Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators.
Operational Collective Intelligence of Humans and Machines
We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions.
"Define Your Terms" : Enhancing Efficient Offensive Speech Classification with Definition
The propagation of offensive content through social media channels has garnered attention of the research community.
The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Models
While large language models (LLMs) are still being adopted to new domains and utilized in novel applications, we are experiencing an influx of the new generation of foundation models, namely multi-modal large language models (MLLMs).
The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance
Large Language Models (LLMs) are regularly being used to label data across many domains and for myriad tasks.
Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks
Previous studies have shed light on the pitfalls of label aggregation and have introduced a handful of practical approaches to tackle this issue.
"Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Models
Large language models (LLMs) have garnered significant attention for their remarkable performance in a continuously expanding set of natural language processing tasks.
The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations
Our study highlights the importance of measuring the bias of LLMs in downstream applications to understand the potential for harm and inequitable outcomes.
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding
We present a simple approach to partial identification using existing causal sensitivity models and show empirically that Caus-Modens gives tighter outcome intervals, as measured by the necessary interval size to achieve sufficient coverage.
Modeling Cross-Cultural Pragmatic Inference with Codenames Duet
Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers and listeners.
Contextualizing Argument Quality Assessment with Relevant Knowledge
Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech.
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning
This shows that prior semantic knowledge is unnecessary; instead, LLMs can leverage the existing patterns in the context to achieve such performance.
Noise Audits Improve Moral Foundation Classification
Morality classification relies on human annotators to label the moral expressions in text, which provides training data to achieve state-of-the-art performance.
Robust Conversational Agents against Imperceptible Toxicity Triggers
Existing work to generate such attacks is either based on human-generated attacks which is costly and not scalable or, in case of automatic attacks, the attack vector does not conform to human-like language, which can be detected using a language model loss.
"Stop Asian Hate!" : Refining Detection of Anti-Asian Hate Speech During the COVID-19 Pandemic
We demonstrate that we are able to identify hate speech that is systematically missed by established hate speech detectors.
Keyword Assisted Embedded Topic Model
By illuminating latent structures in a corpus of text, topic models are an essential tool for categorizing, summarizing, and exploring large collections of documents.
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations.
Low Resource Named Entity Recognition
named-entity-recognition
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Attributing Fair Decisions with Attention Interventions
The widespread use of Artificial Intelligence (AI) in consequential domains, such as healthcare and parole decision-making systems, has drawn intense scrutiny on the fairness of these methods.
Analyzing Race and Country of Citizenship Bias in Wikidata
Through this analysis, we discovered that there is an overrepresentation of white individuals and those with citizenship in Europe and North America; the rest of the groups are generally underrepresented.
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources
In addition, we analyze two downstream models that use ConceptNet as a source for commonsense knowledge and find the existence of biases in those models as well.
Models, Markets, and the Forecasting of Elections
We examine probabilistic forecasts for battleground states in the 2020 US presidential election, using daily data from two sources over seven months: a model published by The Economist, and prices from the PredictIt exchange.
Exacerbating Algorithmic Bias through Fairness Attacks
Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms.
One-shot Learning for Temporal Knowledge Graphs
Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times.
Leveraging Clickstream Trajectories to Reveal Low-Quality Workers in Crowdsourced Forecasting Platforms
In this study, we propose the use of a computational framework to identify clusters of underperforming workers using clickstream trajectories.
Statistical Equity: A Fairness Classification Objective
We formalize our definition of fairness, and motivate it with its appropriate contexts.
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data.
Identifying Distributional Perspective Differences from Colingual Groups
Perspective differences exist among different cultures or languages.
Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification
Cyberbullying is a pervasive problem in online communities.
Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition
We study the bias in several state-of-the-art named entity recognition (NER) models---specifically, a difference in the ability to recognize male and female names as PERSON entity types.
A Survey on Bias and Fairness in Machine Learning
With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them.
Identifying and Analyzing Cryptocurrency Manipulations in Social Media
Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump.
Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications
We apply our solution to sentiment analysis, a task that can benefit from the emoji calibration technique we use in this work.
SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification
To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content.
Feature Selection: A Data Perspective
To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (\url{http://featureselection. asu. edu/}).
Finding Eyewitness Tweets During Crises
These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region.
