Papers with synthesis methods
On the Intractability to Synthesize Factual Inconsistencies in Summarization (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for detecting factual inconsistencies in abstractive summarization are lacking in factual consistency detection. |
| Approach: | They propose to use real model-generated summaries with human annotations to detect factual inconsistencies. |
| Outcome: | The proposed model outperforms the SOTA on CoGenSumm, FactCC, Frank, and SummEval datasets. |
Hierarchical Policy Optimization for Simultaneous Translation of Unbounded Speech (2026.acl-long)
Copied to clipboard
| Challenge: | Existing synthesis methods cannot guarantee data quality. |
| Approach: | They propose a hierarchical reward that balances translation quality and latency objectives by combining supervised fine-tuning data with supervised inputs. |
| Outcome: | The proposed model can reuse key-value caches across both modalities and eliminate redundant feature recomputation. |
RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for generating high-quality reasoning data are limited in quality and availability. |
| Approach: | They propose a method that constructs mathematical operations and generates verifiable graphs that are back-translated into complex problems. |
| Outcome: | The proposed method achieves a 6.3% performance gain over existing methods on LLaMA-3-8B and outperforms others with only half the training data (50k vs. 100k). |
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)
Copied to clipboard
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
| 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. |
Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are a cost-effective and time-consuming way to capture public opinion and behavior, but their outputs are often biased and yield invalid estimates. |
| Approach: | They propose to use large language models to generate survey responses and rectification methods that debias population estimates to find out how human responses are best allocated between them. |
| Outcome: | The proposed methods reduce bias below 5% and increase sample size by up to 14% under a fixed budget. |
HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions (2026.acl-long)
Copied to clipboard
| Challenge: | Multi-Hop Question Answering (MHQA) is a critical benchmark for evaluating the model’s ability to integrate information from diverse sources. |
| Approach: | They propose a framework that synthesizes authentic multi-hop questions without manual annotation without the need for manual guidance. |
| Outcome: | The proposed framework synthesizes bridge and comparison questions without human intervention and achieves comparable or superior quality to human-annotated datasets at a lower cost. |