Algorithmic Harms Associated with Generative Model-Augmented Recommendation Systems
cs.LG, cs.AI, cs.CL, cs.IR
Submitted: 2026-09-27
Updated: 2026-09-27
Terminology
Sources
- GPT-4 Technical Report
- Hallucination of Multimodal Large Language Models: A Survey
- Language (Technology) is Power: A Critical Survey of "Bias" in NLP
- Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
- The Frontiers of Fairness in Machine Learning
- Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions
- Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination
- On Measures of Biases and Harms in NLP
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- LooGLE: Can Long-Context Language Models Understand Long Contexts?
- Red Teaming Visual Language Models
- A Survey on Fairness in Large Language Models
- Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
- Unintended Bias in Language Model-driven Conversational Recommendation
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- Large language models that replace human participants can harmfully misportray and flatten identity groups
- Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs
- Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks