December 31, 2024

In this paper, we presented the notion of positive-sum fairness and argued that larger disparities are not necessarily harmful, as long as it does not come at the expense of a specific subgroup performance. The general performance, standard fairness and positive-sum fairness of four models was analyzed, each leveraging sensitive attributes in a different way. Our study highlights the need for a nuanced understanding of fairness metrics and their implications in real-world applications. Good incorporation

Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum fairness 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Conclusion and References 4.1 Initial results According to traditional group fairness, in assessing the results of the four models shown in figure 3a one could conclude that: M2 improves the overall performance Our results show that M2 outperforms M1 in terms of AUROC. This is in line with our expectation as we are providing

Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.6 Distributed Execution 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6.2