Large Language Models have become the de facto approach to
sequence-to-sequence text generation tasks, but for specialized tasks/domains,
a pretrained LLM lacks specific capabilities to produce accurate or
well-formatted responses. Supervised fine-tuning specializes a LLM by training
it on dataset of example prompts with target responses, but real-world data
tends to be noisy. While many fine-tuning algorithms exist, here we consider a
\emph{data-centric AI} perspective on LLM fine-tuning, studying how to
\emph{systematically} curate the training dataset to improve the LLM produced
via \emph{any} fine-tuning algorithm.
We introduce an automated data curation pipeline CLEAR (Confidence-based LLM
Evaluation And Rectification) for instruction tuning datasets, that can be used
with any LLM and fine-tuning procedure. CLEAR estimates which training data is
low-quality and either filters or corrects it. Automatically identifying which
data to filter or correct is done via LLM-derived confidence estimates, to
ensure only confident modifications to the dataset. Unlike existing data
curation techniques, CLEAR is a comprehensive framework that can improve a
dataset (and trained model outputs) without additional fine-tuning
computations. We don't assume access to a stronger LLM than the model being
fine-tuned (e.g.\ relying on GPT-4 when fine-tuning GPT-3.5), to see whether
CLEAR can meaningfully improve the capabilities of any LLM. Experiments reveal
that CLEAR consistently improves the performance of fine-tuned models across
many datasets and models (like GPT-3.5 and Llama2).

通过自动数据加工管道 CLEAR (Confidence-based LLM Evaluation And Rectification) 对指令调谐数据集进行自动加工，提高模型性能。

用于鲁棒语言模型微调的自动数据整理

Automated Data Curation for Robust Language Model Fine-Tuning

Pre-trained language models have proven their unique powers in capturing
implicit language features. However, most pre-training approaches focus on the
word-level training objective, while sentence-level objectives are rarely
studied. In this paper, we propose Contrastive LEArning for sentence
Representation (CLEAR), which employs multiple sentence-level augmentation
strategies in order to learn a noise-invariant sentence representation. These
augmentations include word and span deletion, reordering, and substitution.
Furthermore, we investigate the key reasons that make contrastive learning
effective through numerous experiments. We observe that different sentence
augmentations during pre-training lead to different performance improvements on
various downstream tasks. Our approach is shown to outperform multiple existing
methods on both SentEval and GLUE benchmarks.

本研究提出了 CLEAR 方法，利用多种句子级别数据增强策略来学习一种噪声不变的句子表示。通过多个实验发现数据增强的不同方法会在下游任务中带来不同的性能提升，并且本方法在 SentEval 和 GLUE 基准测试中的表现优于多种现有方法。

CLEAR: 句子表示的对比学习

CLEAR: Contrastive Learning for Sentence Representation

This paper presents CLEAR, a retrieval model that seeks to complement
classical lexical exact-match models such as BM25 with semantic matching
signals from a neural embedding matching model. CLEAR explicitly trains the
neural embedding to encode language structures and semantics that lexical
retrieval fails to capture with a novel residual-based embedding learning
method. Empirical evaluations demonstrate the advantages of CLEAR over
state-of-the-art retrieval models, and that it can substantially improve the
end-to-end accuracy and efficiency of reranking pipelines.

本论文提出了 CLEAR 模型，它采用神经嵌入匹配模型的语义匹配信号来补充传统的基于词汇精确匹配的模型，通过一种基于残差的嵌入学习方法，明确地训练嵌入来编码语言结构和语义，实验证明了 CLEAR 在终端到端的准确性和效率等方面相比于最先进的检索模型有显著的优势，并且可以显著地提高重排流水线的吞吐量和精度。

用语义残留嵌入补充词汇检索

Complementing Lexical Retrieval with Semantic Residual Embedding

We propose a novel method for explaining the predictions of any classifier.
In our approach, local explanations are expected to explain both the outcome of
a prediction and how that prediction would change if 'things had been
different'. Furthermore, we argue that satisfactory explanations cannot be
dissociated from a notion and measure of fidelity, as advocated in the early
days of neural networks' knowledge extraction. We introduce a definition of
fidelity to the underlying classifier for local explanation models which is
based on distances to a target decision boundary. A system called CLEAR:
Counterfactual Local Explanations via Regression, is introduced and evaluated.
CLEAR generates w-counterfactual explanations that state minimum changes
necessary to flip a prediction's classification. CLEAR then builds local
regression models, using the w-counterfactuals to measure and improve the
fidelity of its regressions. By contrast, the popular LIME method, which also
uses regression to generate local explanations, neither measures its own
fidelity nor generates counterfactuals. CLEAR's regressions are found to have
significantly higher fidelity than LIME's, averaging over 45% higher in this
paper's four case studies.

提出 CLEAR 方法来解释分类器预测结果，该方法可以生成最小改变以翻转预测结果的 w 反事实解释，并使用这些反事实解释来建立本地回归模型，从而提高模型的保真度。相比之下，流行的 LIME 方法没有测量自身的保真度或生成反事实解释。