70 citations · 223 across the 41 of their papers we have counts for
13 papers · 1 filter
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…
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…
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP
Jingwei Ni, Zhijing Jin, Qian Wang +2
Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work -- sometimes negative transfer oc…
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…
Voices of Her: Analyzing Gender Differences in the AI Publication World
Yiwen Ding, Jiarui Liu, Zhiheng Lyu +4
While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topi…
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models
Oana Ignat, Zhijing Jin, Artem Abzaliev +19
Recent progress in large language models (LLMs) has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse t…
OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models
Badr AlKhamissi, Siddharth Verma, Ping Yu +3
In this paper, we conduct a thorough investigation into the reasoning capabilities of Large Language Models (LLMs), focusing specifically on the Open Pretrained Transformers (OPT)…
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…
Natural Language Processing for Policymaking
Zhijing Jin, Rada Mihalcea
Language is the medium for many political activities, from campaigns to news reports. Natural language processing (NLP) uses computational tools to parse text into key information…
Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion
Flavio Schneider, Ojasv Kamal, Zhijing Jin +1
Recent years have seen the rapid development of large generative models for text; however, much less research has explored the connection between text and another "language" of com…