1.6k citations · 12.4k across the 394 of their papers we have counts for
8 papers · 2 filters
Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis
David F. Jenny, Yann Billeter, Mrinmaya Sachan +2
The rapid advancement of Large Language Models (LLMs) has sparked intense debate regarding the prevalence of bias in these models and its mitigation. Yet, as exemplified by both re…
CausalCite: A Causal Formulation of Paper Citations
Ishan Kumar, Zhijing Jin, Ehsan Mokhtarian +4
Citation count of a paper is a commonly used proxy for evaluating the significance of a paper in the scientific community. Yet citation measures are widely criticized for failing t…
Borges and AI
Léon Bottou, Bernhard Schölkopf
Many believe that Large Language Models (LLMs) open the era of Artificial Intelligence (AI). Some see opportunities while others see dangers. Yet both proponents and opponents gras…
Can Large Language Models Infer Causation from Correlation?
Zhijing Jin, Jiarui Liu, Zhiheng Lyu +5
Causal inference is one of the hallmarks of human intelligence. While the field of CausalNLP has attracted much interest in the recent years, existing causal inference datasets in…
All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations
Yuxin Ren, Qipeng Guo, Zhijing Jin +4
Transformer models bring propelling advances in various NLP tasks, thus inducing lots of interpretability research on the learned representations of the models. However, we raise a…
Psychologically-Inspired Causal Prompts
Zhiheng Lyu, Zhijing Jin, Justus Mattern +3
NLP datasets are richer than just input-output pairs; rather, they carry causal relations between the input and output variables. In this work, we take sentiment classification as…
Membership Inference Attacks against Language Models via Neighbourhood Comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin +3
Membership Inference attacks (MIAs) aim to predict whether a data sample was present in the training data of a machine learning model or not, and are widely used for assessing the…
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good
Fernando Gonzalez, Zhijing Jin, Bernhard Schölkopf +3
With the recent advances in natural language processing (NLP), a vast number of applications have emerged across various use cases. Among the plethora of NLP applications, many aca…