Papers with data synthesis framework
Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering (2024.eacl-long)
Copied to clipboard
| Challenge: | Recent approaches to multi-hop question answering rely on in-context learning . however, these models contain billions of parameters making them inefficient at inference time. |
| Approach: | They propose a framework that allows for improving smaller language models with less than 10 human-annotated QA pairs by synthesizing millions of multi-hop questions and claims to fine tune language models. |
| Outcome: | The proposed framework improves model performance on multi-hop question answering and fact verification benchmarks while being almost one-third the size in parameter count. |
DecIF: Improving Instruction-Following through Decomposition (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to obtain high-quality instruction-following data rely heavily on existing documents and existing methods. |
| Approach: | They propose a data synthesis framework, DecIF, which automatically generates accurate and diverse instruction-following data from scratch for supervised fine-tuning and reinforcement learning. |
| Outcome: | Extensive experiments show that the proposed framework can synthesize accurate instruction-following data for both SFT and RL paradigms compared to baselines. |
AIDE: Attribute-Guided MultI-Hop Data Expansion for Data Scarcity in Task-Specific Fine-tuning (2025.acl-industry)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning large language models for specific tasks require extensive seed datasets or struggle to balance task relevance and data diversity. |
| Approach: | They propose a data synthesis framework that uses a multi-hop process to expand very few seed data points while ensuring data diversity and task relevance. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in task-specific fine-tuning by over 30%. |
Scalable Data Synthesis through Human-like Cognitive Imitation and Data Recombination (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) rely on massive amounts of training data, however, the quantity of empirically observed data is limited. |
| Approach: | They propose a data synthesis framework that mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources. |
| Outcome: | The proposed framework mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources thereby enhancing advanced reasoning capabilities in large language models. |
CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture decision-making rationale behind each response. |
| Approach: | They propose a data synthesis framework that synthesizes multi-turn dialogue samples and incrementally generates stage-aligned counseling dialogues. |
| Outcome: | The proposed framework significantly improves therapy fidelity and logical coherence in AI counseling. |
Let’s Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | *Data Synthesis* is a promising way to train a small model with very little labeled data. |
| Approach: | They propose a framework that iteratively extrapolates the errors of a small model trained on a real-world validation dataset using a large language model. |
| Outcome: | The proposed framework reduces the gap between the synthesized dataset and the real data . it improves on multiple NLP tasks and on large models with human-annotated data. |
Building Multi-domain Dialog State Trackers from Single-domain Dialogs (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing multi-domain dialog state tracking models require significant manual effort to define domain relations and collect data. |
| Approach: | They propose a divide-and-conquer (DAC) DST paradigm and a multi-domain dialog synthesis framework to build multi- domain DST models from single-domain dialogues. |
| Outcome: | The proposed paradigm makes building multi-domain DST models easier on unseen domain combinations. |
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models (2025.findings-acl)
Copied to clipboard
| 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. |
Powering Verifiable Learning via Automated Evolutionary Data Synthesis (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to building generalizable verifiable data are task-specific and lack a principled, universal evaluator of verifikatability. |
| Approach: | They propose a task-agnostic, strategy-guided, executably-checkable data synthesis framework that synthesizes problems, diverse candidate solutions and verification artifacts from a single source. |
| Outcome: | The proposed framework synthesizes problems, candidates, and verification artifacts from human-annotated and strategy-induced checks and iteratively discovers strategies. |
Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models have demonstrated that explicit step-by-step thinking can substantially improve performance on complex tasks. |
| Approach: | They propose a model that generates preliminary thoughts for input queries before document retrieval. |
| Outcome: | The proposed model generates preliminary thoughts for input queries before document retrieval. |