Large language models (LLMs) are being increasingly tuned to power complex
generation tasks such as writing, fact-seeking, querying and reasoning.
Traditionally, human or model feedback for evaluating and further tuning LLM
performance has been provided at the response level, enabling faster and more
cost-effective assessments. However, recent works (Amplayo et al. [2022], Wu et
al. [2023]) indicate that sentence-level labels may provide more accurate and
interpretable feedback for LLM optimization. In this work, we introduce methods
to disaggregate response-level labels into sentence-level (pseudo-)labels. Our
approach leverages multiple instance learning (MIL) and learning from label
proportions (LLP) techniques in conjunction with prior information (e.g.,
document-sentence cosine similarity) to train a specialized model for
sentence-level scoring. We also employ techniques which use model predictions
to pseudo-label the train-set at the sentence-level for model training to
further improve performance.
We conduct extensive evaluations of our methods across six datasets and four
tasks: retrieval, question answering, summarization, and math reasoning. Our
results demonstrate improved performance compared to multiple baselines across
most of these tasks. Our work is the first to develop response-level feedback
to sentence-level scoring techniques, leveraging sentence-level prior
information, along with comprehensive evaluations on multiple tasks as well as
end-to-end finetuning evaluation showing performance comparable to a model
trained on fine-grained human annotated labels.

我们介绍了一种将响应级别标签细分为句子级别（伪）标签的方法，该方法利用多实例学习（MIL）和学习标签比例（LLP）技术以及先前信息训练专用模型进行句子级别评分，并利用模型预测对训练集进行伪标签，以进一步提高性能。我们在六个数据集和四个任务上进行了广泛的评估，结果表明在大多数任务中与多个基准方法相比，我们的方法性能有所提高。这项工作是第一个将响应级别反馈应用到句子级别评分技术，并利用句子级别先前信息进行全面评估的工作，同时进行了端到端微调评估，表明性能与基于精细人工标注标签训练的模型相当。

FRACTAL：基于文本标签的细粒度评分

FRACTAL: Fine-Grained Scoring from Aggregate Text Labels

Extractive summarization models require sentence-level labels, which are
usually created heuristically (e.g., with rule-based methods) given that most
summarization datasets only have document-summary pairs. Since these labels
might be suboptimal, we propose a latent variable extractive model where
sentences are viewed as latent variables and sentences with activated variables
are used to infer gold summaries. During training the loss comes
\emph{directly} from gold summaries. Experiments on the CNN/Dailymail dataset
show that our model improves over a strong extractive baseline trained on
heuristically approximated labels and also performs competitively to several
recent models.

本篇文章提出一种基于潜变量的抽取式文本摘要模型，通过使用句子作为潜变量和检索金标准摘要来改善基于启发式标签的抽取式模型，并在 CNN/Dailymail 数据集上得到了良好的结果。