Deep models produce a number of features in each internal layer. A key
problem in applications such as feature compression for remote inference is
determining how important each feature is for the task(s) performed by the
model. The problem is especially challenging in the case of multi-task
inference, where the same feature may carry different importance for different
tasks. In this paper, we examine how effective is mutual information (MI)
between a feature and a model's task output as a measure of the feature's
importance for that task. Experiments involving hard selection and soft
selection (unequal compression) based on MI are carried out to compare the
MI-based method with alternative approaches. Multi-objective analysis is
provided to offer further insight.

深度模型中的特征压缩与远程推理任务之间的关联性，通过互信息作为特征重要性度量，进行硬选择和软选择实验并与其他方法进行比较，提供多目标分析来深入研究。

远程视觉多任务推理的压缩特征选择

Compressive Feature Selection for Remote Visual Multi-Task Inference

Large language models (LLMs) are typically prompted to follow a single
instruction per inference call. In this work, we analyze whether LLMs also hold
the capability to handle multiple instructions simultaneously, denoted as
Multi-Task Inference. For this purpose, we introduce the MTI Bench(Multi-Task
Inference Benchmark), a comprehensive evaluation benchmark encompassing 5,000
instances across 25 tasks. Each task in the MTI Bench involves 2 to 3
sub-tasks. As expected, we first demonstrate that Multi-Task Inference reduces
the total inference time by 1.46 times in average since it does not require
multiple inference calls. Interestingly, contrary to the expectation that LLMs
would perform better when tasks are divided, we find that state-of-the-art
LLMs, such as Llama-2-Chat-70B and GPT-4, show up to 7.3% and 12.4% improved
performance with Multi-Task Inference compared to Single-Task Inference on the
MTI Bench. We release the MTI Bench dataset and our code at this link
this https URL

该研究分析了大型语言模型的多任务推理能力，并介绍了 MTI Bench 作为一个综合评估基准，证明多任务推理相对于单任务推理可以显著减少推理时间并提高性能。

多任务推理：大型语言模型能否同时遵循多个指令？

Multi-Task Inference: Can Large Language Models Follow Multiple  Instructions at Once?

In this paper, we propose Ahead-of-Time (AoT) P-Tuning, a novel
parameter-efficient fine-tuning method for pre-trained Language Models (LMs)
that adds input-dependent bias before each Transformer layer. We evaluate AoT
P-Tuning on GLUE and SuperGLUE benchmarking datasets using RoBERTa and DeBERTa
models, showing that it outperforms BitFit and is comparable or better than
other baseline methods for efficient fine-tuning. Additionally, we assess the
inference overhead of AoT P-Tuning and demonstrate that it introduces
negligible overhead compared to established baseline methods. Our method
enables multi-task inference with a single backbone LM, making it a practical
solution for real-world applications.

本文提出了 Ahead-of-Time (AoT) P-Tuning 方法，即在每个 Transformer 层之前添加输入相关偏置的一种新颖的针对预训练语言模型的参数高效微调方法。在 GLUE 和 SuperGLUE 基准数据集上使用 RoBERTa 和 DeBERTa 模型对 AoT P-Tuning 进行了评估，表明它优于 BitFit，并且与其他基准方法一样或更好。此外，我们评估了 AoT P-Tuning 的推理开销，并证明它引入的开销与已建立的基准方法相比可以忽略不计。我们的方法使得单个骨干语言模型能够进行多任务推理，从而成为现实世界应用的实用解决方案。