  Automated audio captioning (AAC) is an audio-to-text task to describe audio
contents in natural language. Recently, the advancements in large language
models (LLMs), with improvements in training approaches for audio encoders,
have opened up possibilities for improving AAC. Thus, we explore enhancing AAC
from three aspects: 1) a pre-trained audio encoder via consistent ensemble
distillation (CED) is used to improve the effectivity of acoustic tokens, with
a querying transformer (Q-Former) bridging the modality gap to LLM and compress
acoustic tokens; 2) we investigate the advantages of using a Llama 2 with 7B
parameters as the decoder; 3) another pre-trained LLM corrects text errors
caused by insufficient training data and annotation ambiguities. Both the audio
encoder and text decoder are optimized by low-rank adaptation (LoRA).
Experiments show that each of these enhancements is effective. Our method
obtains a 33.0 SPIDEr-FL score, outperforming the winner of DCASE 2023 Task 6A.
