Recent large language models (LLMs) have witnessed significant advancement in
various tasks, including mathematical reasoning and theorem proving. As these
two tasks require strict and formal multi-step inference, they are appealing
domains for exploring the reasoning ability of LLMs but still face important
challenges. Previous studies such as Chain-of-Thought (CoT) have revealed the
effectiveness of intermediate steps guidance. However, such step-wise
annotation requires heavy labor, leading to insufficient training steps for
current benchmarks. To fill this gap, this work introduces MUSTARD, a data
generation framework that masters uniform synthesis of theorem and proof data
of high quality and diversity. MUSTARD synthesizes data in three stages: (1) It
samples a few mathematical concept seeds as the problem category. (2) Then, it
prompts a generative language model with the sampled concepts to obtain both
the problems and their step-wise formal solutions. (3) Lastly, the framework
utilizes a proof assistant (e.g., Lean Prover) to filter the valid proofs. With
the proposed MUSTARD, we present a theorem-and-proof benchmark MUSTARDSAUCE
with 5,866 valid data points. Each data point contains an informal statement,
an informal proof, and a translated formal proof that passes the prover
validation. We perform extensive analysis and demonstrate that MUSTARD
generates validated high-quality step-by-step data. We further apply the
MUSTARDSAUCE for fine-tuning smaller language models. The fine-tuned Llama 2-7B
achieves a 15.41% average relative performance gain in automated theorem
proving, and 8.18% in math word problems. Codes and data are available at
this https URL

该研究论文提出了 MUSTARD，一个数据生成框架，通过三个步骤合成高质量和多样化的定理和证明数据，用于理解大型语言模型在数学推理和定理证明方面的能力。利用 MUSTARD 生成的数据进行 Fine-tuning，可以在自动定理证明和数学问题求解中获得显著的性能提升。

统一合成定理与证明数据的 MUSTARD 方法

MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data

Manually tracking nutritional intake via food diaries is error-prone and
burdensome. Automated computer vision techniques show promise for dietary
monitoring but require large and diverse food image datasets. To address this
need, we introduce NutritionVerse-Synth (NV-Synth), a large-scale synthetic
food image dataset. NV-Synth contains 84,984 photorealistic meal images
rendered from 7,082 dynamically plated 3D scenes. Each scene is captured from
12 viewpoints and includes perfect ground truth annotations such as RGB, depth,
semantic, instance, and amodal segmentation masks, bounding boxes, and detailed
nutritional information per food item. We demonstrate the diversity of NV-Synth
across foods, compositions, viewpoints, and lighting. As the largest
open-source synthetic food dataset, NV-Synth highlights the value of
physics-based simulations for enabling scalable and controllable generation of
diverse photorealistic meal images to overcome data limitations and drive
advancements in automated dietary assessment using computer vision. In addition
to the dataset, the source code for our data generation framework is also made
publicly available at this https URL

通过物理模拟生成多样化逼真的餐食图片，克服数据限制，并驱动计算机视觉自动膳食评估的进步。

NutritionVerse-Synth: 用于饮食摄入估计的开放式合成 2D 食物场景数据集

NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food  Scene Dataset for Dietary Intake Estimation

Introduction: The amount of data generated by original research is growing
exponentially. Publicly releasing them is recommended to comply with the Open
Science principles. However, data collected from human participants cannot be
released as-is without raising privacy concerns. Fully synthetic data represent
a promising answer to this challenge. This approach is explored by the French
Centre de Recherche en {\'E}pid{\'e}miologie et Sant{\'e} des Populations in
the form of a synthetic data generation framework based on Classification and
Regression Trees and an original distance-based filtering. The goal of this
work was to develop a refined version of this framework and to assess its
risk-utility profile with empirical and formal tools, including novel ones
developed for the purpose of this evaluation.Materials and Methods: Our
synthesis framework consists of four successive steps, each of which is
designed to prevent specific risks of disclosure. We assessed its performance
by applying two or more of these steps to a rich epidemiological dataset.
Privacy and utility metrics were computed for each of the resulting synthetic
datasets, which were further assessed using machine learning
approaches.Results: Computed metrics showed a satisfactory level of protection
against attribute disclosure attacks for each synthetic dataset, especially
when the full framework was used. Membership disclosure attacks were formally
prevented without significantly altering the data. Machine learning approaches
showed a low risk of success for simulated singling out and linkability
attacks. Distributional and inferential similarity with the original data were
high with all datasets.Discussion: This work showed the technical feasibility
of generating publicly releasable synthetic data using a multi-step framework.
Formal and empirical tools specifically developed for this demonstration are a
valuable contribution to this field. Further research should focus on the
extension and validation of these tools, in an effort to specify the intrinsic
qualities of alternative data synthesis methods.Conclusion: By successfully
assessing the quality of data produced using a novel multi-step synthetic data
generation framework, we showed the technical and conceptual soundness of the
Open-CESP initiative, which seems ripe for full-scale implementation.

通过开发多步骤的合成数据生成框架并评估其风险效用模型，成功评估了使用该框架生成的数据的质量，展示了开放 - CESP 倡议的技术和概念的可行性。

一个基于距离的原创全合成数据生成方法的统计属性和隐私保证

Statistical properties and privacy guarantees of an original  distance-based fully synthetic data generation method

Question answering (QA) models often rely on large-scale training datasets,
which necessitates the development of a data generation framework to reduce the
cost of manual annotations. Although several recent studies have aimed to
generate synthetic questions with single-span answers, no study has been
conducted on the creation of list questions with multiple, non-contiguous spans
as answers. To address this gap, we propose LIQUID, an automated framework for
generating list QA datasets from unlabeled corpora. We first convert a passage
from Wikipedia or PubMed into a summary and extract named entities from the
summarized text as candidate answers. This allows us to select answers that are
semantically correlated in context and is, therefore, suitable for constructing
list questions. We then create questions using an off-the-shelf question
generator with the extracted entities and original passage. Finally, iterative
filtering and answer expansion are performed to ensure the accuracy and
completeness of the answers. Using our synthetic data, we significantly improve
the performance of the previous best list QA models by exact-match F1 scores of
5.0 on MultiSpanQA, 1.9 on Quoref, and 2.8 averaged across three BioASQ
benchmarks.

LIQUID 是一个自动化框架，用于从未标注的文本中生成列表问答数据集，并可显著提高之前在 MultiSpanQA、Quoref 和 BioASQ 数据集上表现得最好的列表问答模型的精确匹配 F1 分数。