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REQUAL-LM: Reliability and Equity through Aggregation in Large Language Models
Towards addressing this gap, we introduce REQUAL-LM, a novel method for finding reliable and equitable LLM outputs through aggregation.
AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs
Pre-trained Large Language Models (LLMs) have significantly advanced natural language processing capabilities but are susceptible to biases present in their training data, leading to unfair outcomes in various applications.
Sampling-Based Techniques for Training Deep Neural Networks with Limited Computational Resources: A Scalability Evaluation
Making a connection between the two research directions as special cases of approximating matrix multiplications in the context of neural networks, we provide a negative theoretical analysis that shows feedforward approximation is an obstacle against scalability.
