Search Results for author:
Found 25 papers, 12 papers with code
Revisiting gender bias research in bibliometrics: Standardizing methodological variability using Scholarly Data Analysis (SoDA) Cards
These cards will provide a structured framework for documenting and reporting key methodological choices in scholarly data analysis, including author name disambiguation and gender identification procedures.
Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes
Drawing on cognitive science findings related to representativeness heuristics -- where individuals readily recall the representative attribute of a target group in a way that leads to exaggerated beliefs -- we scrutinize LLM responses through this heuristics lens.
MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment
This suggests that MEXA is a reliable method for estimating the multilingual capabilities of English-centric LLMs, providing a clearer understanding of their multilingual potential and the inner workings of LLMs.
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics
For multi-class classification tasks, prompt-based fine-tuning under low-resource scenarios has resulted in performance levels comparable to those of fully fine-tuning methods.
Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictions
The experimental results show that incorporating the birth year does not improve the overall accuracy of gender prediction, especially for names with evolving gender associations.
StereoMap: Quantifying the Awareness of Human-like Stereotypes in Large Language Models
Based on the SCM theory, StereoMap maps LLMs' perceptions of social groups (defined by socio-demographic features) using the dimensions of Warmth and Competence.
Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoning
As language models continue to be integrated into applications of personal and societal relevance, ensuring these models' trustworthiness is crucial, particularly with respect to producing consistent outputs regardless of sensitive attributes.
PyTAIL: Interactive and Incremental Learning of NLP Models with Human in the Loop for Online Data
For natural language processing (NLP) tasks that utilize a collection of features based on lexicons and rules, it is important to adapt these features to the changing data.
What Changed? Investigating Debiasing Methods using Causal Mediation Analysis
Previous work has examined how debiasing language models affect downstream tasks, specifically, how debiasing techniques influence task performance and whether debiased models also make impartial predictions in downstream tasks or not.
What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys
We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge in the context of conversational surveys.
BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation
In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper.
An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events
However, the sparsity of the information as well as the amount of noisy content present a challenge to practitioners to effectively identify shared information on these platforms.
Multilevel Structural Evaluation of Signed Directed Social Networks based on Balance Theory
We expand this modeling approach to incorporate directionality of edges, and consider three levels of analysis: triads, subgroups, and the whole network.
Social and Information Networks Optimization and Control Physics and Society 05C22, 90C90, 90C09, 90C10, 90C35, 05C15, 65K05
Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society
This paper proposes, implements and evaluates a novel, corpus-based approach for identifying categories indicative of the impact of research via a deductive (top-down, from theory to data) and an inductive (bottom-up, from data to theory) approach.
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications
We present a simple, rule-based method for extracting entity networks from the abstracts of scientific literature.
REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning
In this study, we present a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems.
TIGEr: Text-to-Image Grounding for Image Caption Evaluation
This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems.
Enhancing the Measurement of Social Effects by Capturing Morality
We investigate the relationship between basic principles of human morality and the expression of opinions in user-generated text data.
Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics
Using a combination of emphatic, language-specific, and Twitter-specific features for supervised learning resulted in 87{\%} accuracy (F1) for cross-validation of the training set and 63. 4{\%} accuracy when using the test set.
Semi-supervised Named Entity Recognition in noisy-text
In this paper, we report on the solution [ST] we submitted to the WNUT 2016 NER shared task.
Says Who\ldots? Identification of Expert versus Layman Critics' Reviews of Documentary Films
We extend classic review mining work by building a binary classifier that predicts whether a review of a documentary film was written by an expert or a layman with 90. 70{\%} accuracy (F1 score), and compare the characteristics of the predicted classes.
