We empirically study a simple layer-pruning strategy for popular families of
open-weight pretrained LLMs, finding minimal degradation of performance on
different question-answering benchmarks until after a large fraction (up to
half) of the layers are removed. To prune these models, we identify the optimal
block of layers to prune by considering similarity across layers; then, to
"heal" the damage, we perform a small amount of finetuning. In particular, we
use parameter-efficient finetuning (PEFT) methods, specifically quantization
and Low Rank Adapters (QLoRA), such that each of our experiments can be
performed on a single A100 GPU. From a practical perspective, these results
suggest that layer pruning methods can complement other PEFT strategies to
further reduce computational resources of finetuning on the one hand, and can
improve the memory and latency of inference on the other hand. From a
scientific perspective, the robustness of these LLMs to the deletion of layers
implies either that current pretraining methods are not properly leveraging the
parameters in the deeper layers of the network or that the shallow layers play
a critical role in storing knowledge.

通过对热门的开放式权重预训练 LLMs 进行实证研究，我们发现在移除大部分（最多一半）层之前，其在不同的问答基准测试中仅出现轻微性能下降；因此，层剪枝方法可以在减少计算资源的同时提高推理的内存和延迟，并暗示当前的预训练方法未充分利用网络较深层的参数，或者浅层起到了关键的存储知识的作用。

深层网络的不合理无效性

The Unreasonable Ineffectiveness of the Deeper Layers

In open-domain question-answering (ODQA), most existing questions require
single-hop reasoning on commonsense. To further extend this task, we officially
introduce open-domain multi-hop reasoning (ODMR) by answering multi-hop
questions with explicit reasoning steps in open-domain setting. Recently, large
language models (LLMs) have found significant utility in facilitating ODQA
without external corpus. Furthermore, chain-of-thought (CoT) prompting boosts
the reasoning capability of LLMs to a greater extent with manual or automated
paradigms. However, existing automated methods lack of quality assurance, while
manual approaches suffer from limited scalability and poor diversity, hindering
the capabilities of LLMs. In this paper, we propose Self-prompted
Chain-of-Thought (SP-CoT), an automated framework to mass-produce high quality
CoTs of LLMs, by LLMs and for LLMs. SP-CoT introduces an automated generation
pipeline of high quality ODMR datasets, an adaptive sampler for in-context CoT
selection and self-prompted inference via in-context learning. Extensive
experiments on four multi-hop question-answering benchmarks show that our
proposed SP-CoT not only significantly surpasses the previous SOTA methods on
large-scale (175B) LLMs, but also nearly doubles the zero-shot performance of
small-scale (13B) LLMs. Further analysis reveals the remarkable capability of
SP-CoT to elicit direct and concise intermediate reasoning steps by recalling
$\sim$50\% of intermediate answers on MuSiQue-Ans dataset.

在这篇文章中，我们提出了自主启发式链式思考 (SP-CoT) 自动化框架，用于大规模生成高质量的多跳推理数据集并通过上下文学习实现自主启发式推理，实验证明 SP-CoT 在大规模和小规模语言模型上的性能均显著优于前沿方法，并能在中间步骤中召回约 50% 的中间答案。