This report is a reproducibility study of the paper "CDUL: CLIP-Driven
Unsupervised Learning for Multi-Label Image Classification" (Abdelfattah et al,
ICCV 2023). Our report makes the following contributions: (1) We provide a
reproducible, well commented and open-sourced code implementation for the
entire method specified in the original paper. (2) We try to verify the
effectiveness of the novel aggregation strategy which uses the CLIP model to
initialize the pseudo labels for the subsequent unsupervised multi-label image
classification task. (3) We try to verify the effectiveness of the
gradient-alignment training method specified in the original paper, which is
used to update the network parameters and pseudo labels. The code can be found
at this https URL

复现了论文中提到的 CDUL 方法，验证了使用 CLIP 模型初始化伪标签和梯度对齐训练方法在无监督多标签图像分类任务中的有效性。

CDUL 的可重现性研究：基于 CLIP 的无监督学习 在多标签图像分类中的应用

Reproducibility Study of CDUL: CLIP-Driven Unsupervised Learning for  Multi-Label Image Classification

We carried out a reproducibility study of InPars recipe for unsupervised
training of neural rankers. As a by-product of this study, we developed a
simple-yet-effective modification of InPars, which we called InPars-light.
Unlike InPars, InPars-light uses only a freely available language model BLOOM
and 7x-100x smaller ranking models. On all five English retrieval collections
(used in the original InPars study) we obtained substantial (7-30%) and
statistically significant improvements over BM25 in nDCG or MRR using only a
30M parameter six-layer MiniLM ranker. In contrast, in the InPars study only a
100x larger MonoT5-3B model consistently outperformed BM25, whereas their
smaller MonoT5-220M model (which is still 7x larger than our MiniLM ranker),
outperformed BM25 only on MS MARCO and TREC DL 2020. In a purely unsupervised
setting, our 435M parameter DeBERTA v3 ranker was roughly at par with the 7x
larger MonoT5-3B: In fact, on three out of five datasets, it slightly
outperformed MonoT5-3B. Finally, these good results were achieved by re-ranking
only 100 candidate documents compared to 1000 used in InPars. We believe that
InPars-light is the first truly cost-effective prompt-based unsupervised recipe
to train and deploy neural ranking models that outperform BM25.

通过对 InPars 配方进行可重复性研究，我们开发了一种简单但有效的修改版 InPars，称为 InPars-light，它仅使用免费的语言模型 BLOOM 和 7x-100x 较小的排序模型，是第一个真正具有成本效益的基于提示的无监督配方，用于训练和部署神经排名模型，其结果优于 BM25。

InPars-Light: 高效排序器的成本有效的无监督训练

InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers

Combating bias in NLP requires bias measurement. Bias measurement is almost
always achieved by using lexicons of seed terms, i.e. sets of words specifying
stereotypes or dimensions of interest. This reproducibility study focuses on
the original authors' main claim that the rationale for the construction of
these lexicons needs thorough checking before usage, as the seeds used for bias
measurement can themselves exhibit biases. The study aims to evaluate the
reproducibility of the quantitative and qualitative results presented in the
paper and the conclusions drawn thereof. We reproduce most of the results
supporting the original authors' general claim: seed sets often suffer from
biases that affect their performance as a baseline for bias metrics. Generally,
our results mirror the original paper's. They are slightly different on select
occasions, but not in ways that undermine the paper's general intent to show
the fragility of seed sets.

本文通过对文献进行再现性研究，旨在评估种子词汇的建立是否需要经过彻底的检查，以避免其自身的偏见对测量结果产生负面影响，结果与原文大致相似。

[转载] 更差的种子：重现用于偏差度量的词汇方法评估

[Re] Badder Seeds: Reproducing the Evaluation of Lexical Methods for Bias Measurement

The stylistic properties of text have intrigued computational linguistics
researchers in recent years. Specifically, researchers have investigated the
Text Style Transfer (TST) task, which aims to change the stylistic properties
of the text while retaining its style independent content. Over the last few
years, many novel TST algorithms have been developed, while the industry has
leveraged these algorithms to enable exciting TST applications. The field of
TST research has burgeoned because of this symbiosis. This article aims to
provide a comprehensive review of recent research efforts on text style
transfer. More concretely, we create a taxonomy to organize the TST models and
provide a comprehensive summary of the state of the art. We review the existing
evaluation methodologies for TST tasks and conduct a large-scale
reproducibility study where we experimentally benchmark 19 state-of-the-art TST
algorithms on two publicly available datasets. Finally, we expand on current
trends and provide new perspectives on the new and exciting developments in the
TST field.

本文综合评述了最近文本风格迁移的研究进展，提出了一种按类别组织 TST 模型的分类法，并提供了一份最新技术的综合摘要。我们还检验了 19 种最先进的 TST 算法并提供当前趋势的新视角。