2 citations · 7 across the 10 of their papers we have counts for
10 papers
GPTZero: Robust Detection of LLM-Generated Texts
George Alexandru Adam, Alexander Cui, Edwin Thomas +7
While historical considerations surrounding text authenticity revolved primarily around plagiarism, the advent of large language models (LLMs) has introduced a new challenge: disti…
Epistemic Integrity in Large Language Models
Bijean Ghafouri, Shahrad Mohammadzadeh, James Zhou +8
Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks fo…
A Guide to Misinformation Detection Data and Evaluation
Camille Thibault, Jacob-Junqi Tian, Gabrielle Peloquin-Skulski +7
Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this, we have curated the largest collection of (m…
Web Retrieval Agents for Evidence-Based Misinformation Detection
Jacob-Junqi Tian, Hao Yu, Yury Orlovskiy +7
This paper develops an agent-based automated fact-checking approach for detecting misinformation. We demonstrate that combining a powerful LLM agent, which does not have access to…
Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models
Raeid Saqur, Anastasis Kratsios, Florian Krach +5
We propose MoE-F - a formalized mechanism for combining pre-trained Large Language Models (LLMs) for online time-series prediction by adaptively forecasting the best weighting…
Template-Based Probes Are Imperfect Lenses for Counterfactual Bias Evaluation in LLMs
Farnaz Kohankhaki, D. B. Emerson, Jacob-Junqi Tian +2
Bias in large language models (LLMs) has many forms, from overt discrimination to implicit stereotypes. Counterfactual bias evaluation is a widely used approach to quantifying bias…
Party Prediction for Twitter
Kellin Pelrine, Anne Imouza, Zachary Yang +8
A large number of studies on social media compare the behaviour of users from different political parties. As a basic step, they employ a predictive model for inferring their polit…
On The Role of Reasoning in the Identification of Subtle Stereotypes in Natural Language
Jacob-Junqi Tian, Omkar Dige, D. B. Emerson +1
Large language models (LLMs) are trained on vast, uncurated datasets that contain various forms of biases and language reinforcing harmful stereotypes that may be subsequently inhe…
Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting?
Omkar Dige, Jacob-Junqi Tian, David Emerson +1
As the breadth and depth of language model applications continue to expand rapidly, it is increasingly important to build efficient frameworks for measuring and mitigating the lear…
Soft-prompt Tuning for Large Language Models to Evaluate Bias
Jacob-Junqi Tian, David Emerson, Sevil Zanjani Miyandoab +3
Prompting large language models has gained immense popularity in recent years due to the advantage of producing good results even without the need for labelled data. However, this…