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From the 1 of 23 linked papers with an AI index.

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20242026
most citedConcept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution

7 citations · 9 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CL20261 cited

Accidental Vulnerability: Factors in Fine-Tuning that Shift Model Safeguards

Punya Syon Pandey, Samuel Simko, Kellin Pelrine +1

As large language models (LLMs) gain popularity, their vulnerability to adversarial attacks emerges as a primary concern. While fine-tuning models on domain-specific datasets is of…

cs.CL2025

: A Social Media User Dataset for LLM Persona Evaluation and Training

Aurélien Bück-Kaeffer, Je Qin Chooi, Dan Zhao +5

Large language models (LLMs) offer promising capabilities for simulating social media dynamics at scale, enabling studies that would be ethically or logistically challenging with h…

cs.CL2025

Veracity: An Open-Source AI Fact-Checking System

Taylor Lynn Curtis, Maximilian Puelma Touzel, William Garneau +8

The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper introduces Veracity, an open-source AI…

cs.CL2025

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…

cs.CL2024

Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation

Mauricio Rivera, Jean-François Godbout, Reihaneh Rabbany +1

Large Language Models have emerged as prime candidates to tackle misinformation mitigation. However, existing approaches struggle with hallucinations and overconfident predictions.…