Concluding Remarks on Consistency Large Language Models and Future Directions

CLLMs offer a simpler, more efficient approach to LLM acceleration without extra architectures or draft models, achieving significant speedup gains.

Table of Links

Abstract and 1 Introduction

2. Related Work

3. Methodology and 3.1. Preliminary: Jacobi Decoding

3.2. Consistency Large Language Models (CLLMs)

3.3. Acceleration Mechanisms in CLLMs

4. Experiments

4.1. Evaluations

4.2. Acceleration Mechanisms in CLLMs

4.3. Ablation Studies

4.4. Limitations and Discussion

5. Conclusion, Impact Statement, and References

A. Illustration of Consistency Loss Learning Objectives

B. Comparison with Baseline Algorithms

C. Pesudo Code for Jacobi Decoding with KV Cache

5. Conclusion

In this work, we introduce CLLMs, a new family of LLMs that excel in efficient parallel decoding, designed to significantly enhance the efficiency of Jacobi decoding. Unlike

other existing techniques for efficient LLM inference, which often require either additional architectural components (Cai ...