Aligning large language models(LLMs) with human is a critical step in
effectively utilizing their pre-trained capabilities across a wide array of
language tasks. Current instruction tuning practices often rely on expanding
dataset size without a clear strategy for ensuring data quality, which can
inadvertently introduce noise and degrade model performance. To address this
challenge, we introduce Nuggets, a novel and efficient methodology that employs
one shot learning to select high-quality instruction data from expansive
datasets. Nuggets assesses the potential of individual instruction examples to
act as effective one shot examples, thereby identifying those that can
significantly enhance diverse task performance. Nuggets utilizes a scoring
system based on the impact of candidate examples on the perplexity of a diverse
anchor set, facilitating the selection of the most beneficial data for
instruction tuning. Through rigorous testing on two benchmarks, including
MT-Bench and Alpaca-Eval, we demonstrate that instruction tuning with the top
1% of Nuggets-curated examples substantially outperforms conventional methods
that use the full dataset. These findings advocate for a data selection
paradigm that prioritizes quality, offering a more efficient pathway to align
LLMs with humans.

利用奇点法选择高质量的训练数据进行指令调整以优化大型语言模型的性能。在两个基准测试中，采用奇点法选择的前 1% 的样本比传统方法使用完整数据集要表现更好，强调了优先考虑质量的数据选择范例可以更高效地对齐大型语言模型和人类。

大型语言模型的一次学习作为指导数据矿工

One Shot Learning as Instruction Data Prospector for Large Language  Models

Existing dialogue quality evaluation systems can return a score for a given
system turn from a particular viewpoint, e.g., engagingness. However, to
improve dialogue systems by locating exactly where in a system turn potential
problems lie, a more fine-grained evaluation may be necessary. We therefore
propose an evaluation approach where a turn is decomposed into nuggets (i.e.,
expressions associated with a dialogue act), and nugget-level evaluation is
enabled by leveraging an existing turn-level evaluation system. We demonstrate
the potential effectiveness of our evaluation method through a case study.

通过将对话系统的对话转换为表达与对话行为相关的片段，并借助现有的对话水平评估系统，我们提出了一种评估方法，从而在定位潜在问题所在时能够实现更精细的评估。通过案例研究，我们展示了我们评估方法的潜在有效性。