SYNTHETIC DATA GENERATION

Beyond Sample-Level Feedback: Using Reference-Level Feedback to Guide Data Synthesis (2026.eacl-long)

Challenge:	'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper.
Approach:	This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems.
Outcome:	This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation.

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Shuhaib Mehri, Xiusi Chen, Heng Ji, Dilek Hakkani-Tür

MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data (2026.findings-acl)

Challenge:	High-quality instruction-tuning data is crucial for Large Language Models (LLMs) but it imposes a quality ceiling where models trained on the data cannot outperform the LLM generating it.
Approach:	They propose a paradigm that extracts desirable characteristics from carefully curated reference samples to guide the synthesis of higher-quality instruction-response pairs.
Outcome:	The proposed paradigm outperforms traditional sample-level feedback methods and generalizes across model architectures.

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Yaobin Ling, Xiaoqian Jiang, Yejin Kim

Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation (2026.findings-acl)

Challenge:	Existing models for tabular data generation require large amounts of data to train effectively.
Approach:	They propose a framework to generate tabular data powered by large language models that emulates a Generative Adversarial Network.
Outcome:	The proposed framework outperforms state-of-the-art models while keeping privacy of real data.

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Qian Ma, Sarah Rajtmajer

SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task (D19-52)

Challenge:	Large language models (LLMs) are a powerful tool for creating synthetic replicas of private text.
Approach:	They propose a method for creating privacy-preserving synthetic data using private seeds and a formal differential privacy mechanism.
Outcome:	The proposed method achieves high fidelity to private data while providing strong privacy protection.

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Jitao Xu, TuAnh Nguyen, MinhQuang Pham, Josep Crego, Jean Senellart

Text Generation

Challenge:	Several data sets were provided for the task, but the data was limited and the distance and richness of the language pair were very challenging.
Approach:	They describe the SYSTRAN neural MT systems employed for the 6th Workshop on Asian Translation (WAT) they use the neural Transformer architecture learned over the provided resources and perform synthetic data generation experiments which aim at alleviating the data scarcity problem.
Outcome:	The proposed systems rank first according to automatic evaluations based on the neural Transformer architecture learned over the provided resources.

A Little Human Data Goes A Long Way (2025.acl-short)

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Dhananjay Ashok, Jonathan May

Challenge:	Existing methods to replace human annotation are expensive and limited.
Approach:	They investigate the use of synthetic data in Fact Verification and Evidence-based Question Answering by replacing human-generated data with synthetic points on eight diverse datasets.
Outcome:	The proposed method shows promise but performance declines when replacing up to 90% of training data with synthetic data are severe . the proposed method can be used to improve models trained on purely synthetic data by including as few as 125 human-generated data points.

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis (2026.acl-demo)

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Zhichao Shi, Cehao Yang, Hao Zhou, Xiaojun Wu, Huajie Li, Xuhui Jiang, Chengjin Xu, Yuanzhuo Wang, Jian Guo

Multimodality

Challenge:	Existing synthetic data tools are limited by convoluted workflows, fragmented data standards, and limited scalability across modalities.
Approach:	They develop an open-source framework that aims to reduce the technical barrier to synthetic data generation and subsequent model training.
Outcome:	The proposed framework achieves an optimal balance between generation efficiency and data quality.

CASPER: Bridging Discrete and Continuous Prompt Optimization through Feedback-Guided Gradient Descent (2026.eacl-industry)

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Aryan Jain, Pushpendu Ghosh, Promod Yenigalla

Building Adaptive Acceptability Classifiers for Neural NLG (2021.emnlp-main)

Challenge:	Existing pipelines for generative tasks require extensive manual effort and domain expertise to achieve task-optimal performance.
Approach:	They propose a framework bridging discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space.
Outcome:	The proposed framework bridges discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space.

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Soumya Batra, Shashank Jain, Peyman Heidari, Ankit Arun, Catharine Youngs, Xintong Li, Pinar Donmez, Shawn Mei, Shiunzu Kuo, Vikas Bhardwaj, Anuj Kumar, Michael White

Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation (2026.eacl-long)

Challenge:	Existing approaches to generate synthetic data using simple sentence transformations and/or model-based techniques may not generate realistic error samples with respect to the NLG models.
Approach:	They propose a framework to train models to classify acceptability of responses generated by natural language generation models using a 2-stage approach . they use existing sentence transformations to generate samples that better resemble the output of the generation models.
Outcome:	The proposed approach outperforms existing techniques and can be used in few-shot settings using self-training.

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Thomas F Burns, Letitia Parcalabescu, Stephan Waeldchen, Michael Barlow, Gregor Ziegltrum, Volker Stampa, Bastian Harren, Björn Deiseroth

Learning Compact Metrics for MT (2021.emnlp-main)

Challenge:	Recent studies show that data quality can significantly boost performance and training efficiency for large language models.
Approach:	They propose a German-language dataset curation pipeline that combines heuristic and model-based filtering techniques with synthetic data generation.
Outcome:	The proposed pipeline can be used to create a large-scale German pre-training dataset using common Crawl web data, fineweb2 and synthetically generated data conditioned on real, organic web data.

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Amy Pu, Hyung Won Chung, Ankur Parikh, Sebastian Gehrmann, Thibault Sellam

Multilinguality

Challenge:	Recent advances in machine translation and multilingual text generation have led researchers to adopt trained metrics such as COMET or BLEURT, which treat evaluation as a regression problem and use representations from multilingual pre-trained models such as XLM-RoBERTa or mBERT.
Approach:	They propose to use multilingual model capacity to improve model performance by transferring knowledge from one teacher to multiple students trained on related languages.
Outcome:	The proposed model yields 10.5% improvement over vanilla fine-tuning and reaches 92.6% of RemBERT’s performance using only a third of its parameters.

GerMedIQ: A Resource for Simulated and Synthesized Anamnesis Interview Responses in German (2025.acl-srw)

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Justin Hofenbitzer, Sebastian Schöning, Belle Sebastian, Jacqueline Lammert, Luise Modersohn, Martin Boeker, Diego Frassinelli

Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction (2023.emnlp-main)

Challenge:	Text corpora in non-English clinical contexts is scarce due to privacy restrictions and restricted access to secure environments.
Approach:	They propose to use Large Language Models to generate synthetic data using a German medical interview questions corpus.
Outcome:	The proposed dataset generates comparable responses to human-generated questions.

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Martin Josifoski, Marija Sakota, Maxime Peyrard, Robert West

Information Extraction & Text Mining

Challenge:	Large language models (LLMs) have great potential for synthetic data generation.
Approach:	They show that large language models can generate useful data even for complex tasks . they use a symmetric task difficulty asymmetry to prompt an LLM to generate plausible input text for a target output structure.
Outcome:	The proposed approach outperforms existing models by a substantial margin on closed information extraction tasks with 1.8M data points and 770M parameters.

DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows (2024.acl-long)

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Ajay Patel, Colin Raffel, Chris Callison-Burch

RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis (2025.emnlp-main)

Challenge:	Large language models (LLMs) have become a dominant tool for NLP researchers in a wide range of tasks.
Approach:	They propose an open source Python library that allows researchers to write simple code to implement powerful LLM workflows.
Outcome:	The proposed library is open source and can be used to implement powerful LLM workflows.

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Jianwei Wang, Chengming Shi, Junyao Yang, Haoran Li, Qianli Ma, Huiping Zhuang, Cen Chen, Ziqian Zeng

FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data (2025.naacl-long)

Challenge:	Existing solutions to fine-tune large language models for domain-specific tasks are ineffective in addressing privacy concerns.
Approach:	They propose a privacy-preserving framework that fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation.
Outcome:	The proposed framework fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation.

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Deren Lei, Yaxi Li, Siyao Li, Mengya Hu, Rui Xu, Ken Archer, Mingyu Wang, Emily Ching, Alex Deng

OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)

Challenge:	Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data.
Approach:	They propose a method that leverages multi-hop reasoning on context graphs extracted from documents to generate complex multi-level claims without relying on LLMs to decide data labels.
Outcome:	The proposed model outperforms GPT-4-o on the LLM-Aggrefact benchmark with much smaller model size.

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Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu

Pretraining Language Models Using Translationese (2024.emnlp-main)

Challenge:	Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability.
Approach:	They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks.
Outcome:	The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity.

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Meet Doshi, Raj Dabre, Pushpak Bhattacharyya

Text Generation

Challenge:	a recent study shows that large language models perform well in low-resource languages . a vast majority of languages don't have comparable data as compared to English .
Approach:	They propose to use Translationese as synthetic data for pre-training language models for low-resource languages.
Outcome:	The proposed method reduces performance of LMs trained on clean data in Indian languages . the proposed model performs better in English than in other languages, but is not comparable to English.

ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification (2025.findings-naacl)

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Yaswanth M, Vaibhav Singh, Ayush Maheshwari, Amrith Krishna, Ganesh Ramakrishnan

Advancing Post-OCR Correction: A Comparative Study of Synthetic Data (2024.findings-acl)

Challenge:	Existing frameworks for large language models (LLMs) generate high-quality synthetic data that can be used to supplement training data or surpass crowd-sourced annotations.
Approach:	They propose a framework that iteratively induces rules and generates synthetic data for text classification.
Outcome:	The proposed framework outperforms existing models on in-context learning and fine-tuning settings by using augmented data.

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Shuhao Guan, Derek Greene

Multimodality

Challenge:	Optical Character Recognition (OCR) is a useful tool for preserving cultural heritage and supporting downstream tasks.
Approach:	They propose an algorithm that leverages computer vision feature detection algorithms to calculate glyph similarity for constructing post-OCR synthetic data.
Outcome:	The proposed method significantly reduces CERs in low-resource languages without manually annotating data.

Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation (2025.acl-long)

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Xiang Geng, Zhejian Lai, Jiajun Chen, Hao Yang, Shujian Huang

Machine Translation

Challenge:	Quality Estimation (QE) models evaluate the quality of machine translations without reference translations, serving as the reward models for the translation task.
Approach:	They propose a framework for alleviating distribution shift in synthetic QE data . they employ a constrained beam search algorithm and distinct generation models to enhance translation diversity.
Outcome:	The proposed framework outperforms SOTA baselines like CometKiwi in supervised and unsupervised settings.

On Synthetic Data Strategies for Domain-Specific Generative Retrieval (2025.acl-long)

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Haoyang Wen, Jiang Guo, Yi Zhang, Jiarong Jiang, Zhiguo Wang

A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages (2025.emnlp-main)

Challenge:	Generative retrieval models can be used to generate ranked lists of potentially relevant document identifiers for a user query.
Approach:	They propose a synthetic data generation strategy for a two-stage training framework that focuses on learning to decode document identifiers from queries and a strategy for mining hard negatives based on initial model's predictions.
Outcome:	The proposed model can generate ranked lists of potentially relevant document identifiers for a user query and then refine ranking through preference learning.

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Tatiana Anikina, Jan Cegin, Jakub Simko, Simon Ostermann

Low-Resource NLP

Challenge:	Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models.
Approach:	They evaluate the performance of large language models and their generation strategies in 11 different languages using 3 NLP tasks and 4 open-source LLMs.
Outcome:	The proposed generation strategies and their combinations yield strong results across 11 languages, including several extremely low-resource ones.

Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval (2021.emnlp-main)

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Jing Lu, Gustavo Hernandez Abrego, Ji Ma, Jianmo Ni, Yinfei Yang

Examining False Positives under Inference Scaling for Mathematical Reasoning (2025.emnlp-main)

Challenge:	Existing neural firststage retrieval models overcome lexical gap issue by projecting query and document to a shared dense space.
Approach:	They propose a multi-stage framework for neural passage retrieval using synthetic data, negative sampling, and fusion techniques.
Outcome:	The proposed framework improves retrieval accuracy and enhances the negative contrast in both stages.

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Yu Wang, Nan Yang, Liang Wang, Furu Wei, Fuli Feng

Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations (2023.emnlp-main)

Challenge:	Recent advances in language models have led to significant improvements in mathematical reasoning across benchmarks.
Approach:	They analyze the prevalence of false positives in language models by using heuristic evaluation methods . they find that false positive models produce correct final answers but with flawed deduction paths .
Outcome:	The proposed model performance improvements are based on the proposed model and its evaluation metrics.

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Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, Ming Yin

On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)

Challenge:	Recent studies have explored using large language models to generate synthetic datasets . however, the effectiveness of the LLM-generated synthetic data is inconsistent across different classification tasks.
Approach:	They propose to use large language models to generate synthetic datasets to better understand factors that moderate the effectiveness of LLM-generated synthetic data.
Outcome:	The results show that subjectivity is negatively associated with the performance of the model trained on synthetic data.

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Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, Haobo Wang

Challenge:	Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation.
Approach:	They propose a generic workflow for LLM-driven synthetic data generation.
Outcome:	The proposed workflows highlight gaps in existing research and outline avenues for future studies.

SynthAgent: Adapting Web Agents with Synthetic Supervision (2026.acl-long)

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Zhaoyang Wang, Yiming Liang, Xuchao Zhang, Qianhui Wu, Siwei Han, Anson Bastos, Rujia Wang, Chetan Bansal, Baolin Peng, Jianfeng Gao, Saravan Rajmohan, Huaxiu Yao

Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking (2024.findings-emnlp)

Challenge:	Existing studies have focused on synthetic supervision but have encountered data quality issues.
Approach:	They propose a fully synthetic supervision framework that aims at improving data quality via dual refinement of both tasks and trajectories.
Outcome:	The proposed framework outperforms existing methods on standardized benchmarks and shows promising results on a standardized test.

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James Finch, Jinho Choi

Dialogue Systems & Conversational Agents

Challenge:	Existing zero-shot dialogue state tracking datasets are limited in the number of domains and slot types they cover due to the high costs of data collection.
Approach:	They propose a fully automatic approach that generates synthetic zero-shot dialogue state tracking datasets.
Outcome:	The proposed approach can generate dialogues across 1,000+ domains with silver-standard dialogue state annotations and slot descriptions.

PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation (2025.emnlp-main)

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Tao Fan, Guoqiang Ma, Yuanfeng Song, Lixin Fan, Qiang Yang

IR2: Information Regularization for Information Retrieval (2024.lrec-main)

Challenge:	Large Language Models (LLMs) are becoming more popular and are gaining widespread use in artificial intelligence.
Approach:	They propose a unified framework that addresses both privacy preservation and model compression in federated settings.
Outcome:	The proposed framework maintains competitive performance comparable to full-sized LLMs while ensuring robust privacy protection through its federated architecture.

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Jianyou Wang, Kaicheng Wang, Xiaoyue Wang, Weili Cao, Ramamohan Paturi, Leon Bergen

Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving (2025.findings-acl)

Challenge:	Effective information retrieval (IR) in settings with limited training data remains a challenging task.
Approach:	They propose a technique for reducing overfitting during synthetic data generation . they use DORIS-MAE, ArguAna, and WhatsThatBook as examples .
Outcome:	The proposed technique outperforms previous methods and reduces cost by 50% on three recent IR tasks characterized by complex queries.

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Sara Rajaee, Kumar Pratik, Gabriele Cesa, Arash Behboodi

Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains (2024.findings-emnlp)

Challenge:	Recent methods for AI reasoning require applying variants of reinforcement learning (RL) on rolled out trajectories, even for step-wise rewards, or large quantities of human-annotated trajectory data.
Approach:	They propose a verifier-in-the-loop design that uses an automated verifier to give intermediate feedback at each step of the reasoning process.
Outcome:	The proposed model improves on the Automatic Theorem Proving task using Lean as the verifier.

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Krithika Ramesh, Nupoor Gandhi, Pulkit Madaan, Lisa Bauer, Charith Peris, Anjalie Field

Multilingual Generation and Answering of Questions from Texts and Knowledge Graphs (2023.findings-emnlp)

Challenge:	a lack of anonymization of sensitive text data hinders development of NLP tools . poorly anonymized sensitive data cannot be easily shared with annotators or external researchers .
Approach:	They propose to use synthetic data to generate differentially private language models in place of real data to facilitate NLP development without compromising privacy.
Outcome:	The proposed model can be used to train public models without compromising privacy.

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Kelvin Han, Claire Gardent

Multimodality

Challenge:	Existing methods for QG-QA are limited to English, but can be used in other languages.
Approach:	They propose to bring multilinguality to multimodal QG-QA by using Brazilian Portuguese and Russian data.
Outcome:	The proposed approach outperforms a baseline on English and can handle both languages.

LLM-based Code-Switched Text Generation for Grammatical Error Correction (2024.emnlp-main)

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Tom Potter, Zheng Yuan

Multilinguality

Challenge:	Code-switching (CSW) is a part of multilingual conversation and is gaining popularity in social and professional settings.
Approach:	They propose to use synthetic data to generate a model capable of correcting grammatical errors in CSW texts.
Outcome:	The proposed model improves on existing systems on an authentic dataset from English as a second language learners.

Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation (2025.findings-emnlp)

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Guangzeng Han, Weisi Liu, Xiaolei Huang

DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models (2025.findings-acl)

Challenge:	Genetic Prompt combines genetic algorithms with Large Language Models to augment synthetic data generation.
Approach:	They propose a framework that combines genetic algorithms with LLMs to augment synthetic data generation.
Outcome:	The proposed framework outperforms state-of-the-art models and shows robust performance across generator models.

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Ying Zhou, Xinyao Wang, Yulei Niu, Yaojie Shen, Lexin Tang, Fan Chen, Ben He, Le Sun, Longyin Wen

Neural Machine Translation between Low-Resource Languages with Synthetic Pivoting (2024.lrec-main)

Challenge:	Recent advances in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis.
Approach:	They propose a controllable data synthesis framework based on variational autoencoder which leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution.
Outcome:	The proposed framework generates high-quality data with performance exceeding that of real data by 2%–7% on seven real-world datasets.

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Khalid Ahmed, Jan Buys

Text Generation

Challenge:	Pivot-based neural machine translation systems overcome data scarcity by including a high-resource pivot language in the process of translating between low-resourced languages.
Approach:	They propose a novel approach to pivot-based translation in which pivot sentences are generated synthetically from both the source and target languages.
Outcome:	The proposed approach improves pivot-based systems translating between low-resource Southern African languages by up to 5.6 BLEU points after fine-tuning.

RPDR: A Round-trip Prediction-Based Data Augmentation Framework for Long-Tail Question Answering (2025.emnlp-main)

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Yiming Zhang, Siyue Zhang, Junbo Zhao, Chen Zhao

Retrieval-enriched zero-shot image classification in low-resource domains (2024.emnlp-main)

Challenge:	Long-tail question answering presents significant challenges for large language models due to limited ability to acquire and accurately recall less common knowledge.
Approach:	They propose a data augmentation framework that selects high-quality easy-to-learn training data to enhance dense retrieval models.
Outcome:	The proposed framework improves on two long-tail retrieval benchmarks, PopQA and EntityQuestion, and shows that it outperforms existing retrievers on extremely long-tailed questions.

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Nicola Dall’Asen, Yiming Wang, Enrico Fini, Elisa Ricci

Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance (2025.findings-emnlp)

Challenge:	Low-resource domains are those where data or annotations are scarce.
Approach:	They propose a retrieval-based method for low-resource domains that trains without training . they use web-crawled databases to retrieve relevant textual information from query images .
Outcome:	The proposed method outperforms existing training-based methods in low-resource domains . it retrieves relevant textual information from large web-crawled databases .

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Reza Esfandiarpoor, George Zerveas, Ruochen Zhang, Macton Mgonzo, Carsten Eickhoff, Stephen Bach

Mixture-of-Personas Language Models for Population Simulation (2025.findings-acl)

Challenge:	a new approach to training with binary relevance labels uses synthetic data . contrastive learning with binary correlations leaves out subtle nuances useful for ranking .
Approach:	They propose to use waterstein distance as a loss function for training transformer-based retrievers with graduated relevance labels instead of real documents.
Outcome:	The proposed method outperforms conventional training with InfoNCE by a large margin on MARCO and BEIR benchmarks without using real documents.

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Ngoc Bui, Hieu Trung Nguyen, Shantanu Kumar, Julian Theodore, Weikang Qiu, Viet Anh Nguyen, Rex Ying

PROOD: A Simple LLM Out-of-Distribution Guardrail Leveraging Response Semantics (2025.findings-emnlp)

Challenge:	Pretrained LLMs fail to capture behavioral diversity of target populations due to inherent variability across individuals and groups.
Approach:	They propose a probabilistic prompting method that aligns LLM responses with the target population.
Outcome:	Experiments show that the proposed method outperforms competing methods in alignment and diversity metrics.

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Joshua Tint

Exploring Quality and Diversity in Synthetic Data Generation for Argument Mining (2025.emnlp-main)

Challenge:	Existing OOD methods often struggle with deliberately obfuscated, context-dependent, or superficially benign prompts.
Approach:	They propose a framework that jointly analyzes LLM prompts and their outputs to improve semantic understanding.
Outcome:	The proposed framework outperforms existing OOD methods on three benchmarks and improves F1 scores by up to 6.3 points.

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Jianzhu Bao, Yuqi Huang, Yang Sun, Wenya Wang, Yice Zhang, Bojun Jin, Ruifeng Xu

Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data (2024.lrec-main)

Challenge:	Argument Mining (AM) is hindered by the scarcity of structure-annotated datasets, which are expensive to create manually.
Approach:	They propose to use quality-oriented synthesis and diversity-oriented approach to generate argumentative texts with diverse topics and argument structures.
Outcome:	The proposed approach significantly improves existing models in full-data and low-resource settings.

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Shinka Mori, Oana Ignat, Andrew Lee, Rada Mihalcea

Theorem Prover as a Judge for Synthetic Data Generation (2025.acl-long)

Challenge:	Synthetic data generation has the potential to impact domains with scarce data, but we need to understand how different demographics are represented in it.
Approach:	They develop a procedure to generate depression data using GPT-3 and analyze it to uncover the types of stressors it assigns to demographic groups.
Outcome:	The proposed procedure produces depression data using GPT-3, and compares it to a human-generated dataset.

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Joshua Ong Jun Leang, Giwon Hong, Wenda Li, Shay B Cohen

A Survey on Efficient Large Language Model Training: From Data-centric Perspectives (2025.acl-long)

Challenge:	Recent studies show that large language models are increasingly capable of tackling mathematical problems.
Approach:	They propose an approach that iteratively refines theorem prover formalisation to mitigate errors.
Outcome:	The proposed method increases execution rate on the Lean prover from 60% to 87%, while human annotation is replaced with theorem prover feedback.

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Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang

Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)

Challenge:	achieving data-efficient post-training of Large Language Models is a key research question.
Approach:	They propose a taxonomy of data-efficient LLM post-training methods from a data-centric perspective.
Outcome:	The proposed methods cover data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems.

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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang

Challenge:	Existing methods for training effective AI agents often resort to synthetic data generation.
Approach:	They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance .
Outcome:	The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset.

A Survey of Toxicity Mitigation Strategies for Multilingual Language Models (2026.findings-acl)

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Soham Dan, Himanshu Beniwal, Thomas Hartvigsen