Papers with iterative framework
GRS: Combining Generation and Revision in Unsupervised Sentence Simplification (2022.findings-acl)
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| Challenge: | Existing methods for sentence simplification are supervised or unsupervised . paraphrasing captures complex edit operations, while revision-based methods provide more control and interpretability. |
| Approach: | They propose an unsupervised approach to sentence simplification that combines text generation and text revision. |
| Outcome: | The proposed method improves on the Newsela and ASSET datasets. |
SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning (2026.findings-eacl)
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| Challenge: | Existing red-teaming frameworks do not cover all the risks associated with arbitrary black-box LLMs. |
| Approach: | They propose a generic red-teaming framework for arbitrary black-box LLM agents that iteratively constructs and refines model-based adversarial attacks based on the execution trajectories of former attempts. |
| Outcome: | The proposed model improves attack success rate by 100%, surpassing the 671B Deepseek-R1 model. |
CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation (2024.emnlp-main)
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| Challenge: | Recent studies have focused on constructing substantial quantities of IFT data with minimal human effort. |
| Approach: | They propose a multi-agent cooperation framework for the improvement of IFT responses for large language models using a debate-advise-edit-judge paradigm. |
| Outcome: | The proposed framework outperforms baseline models on unseen tasks and shows that it can improve instruction-following capabilities on large language models. |
A Relaxed Matching Procedure for Unsupervised BLI (2020.acl-main)
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| Challenge: | Recent studies have shown that unsupervised bilingual lexicon induction is even on par with supervised methods. |
| Approach: | They propose a relaxed matching procedure to find a more precise matching between two languages by aligning source and target embedding space bidirectionally. |
| Outcome: | The proposed method significantly outperforms previous unsupervised methods on standard benchmarks. |
Collaborative Beam Search: Enhancing LLM Reasoning via Collective Consensus (2025.emnlp-main)
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| Challenge: | Existing approaches to improve the reasoning capabilities of large language models (LLMs) depend on domain-specific external verifiers or self-evaluation which is brittle and prompt-sensitive. |
| Approach: | They propose a framework that harnesses the collective intelligence of multiple large language models across both generation and verification stages. |
| Outcome: | The proposed framework outperforms singlemodel scaling and multi-model ensemble baselines on six tasks by over 4 percentage points in average accuracy. |
Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)
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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang
| Challenge: | Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking. |
| Approach: | They propose an iterative adversarial training method that incorporates three key innovations to address these challenges. |
| Outcome: | Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%. |
S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA (2026.acl-long)
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| Challenge: | Retrieval-augmented generation grounds language models in external evidence, but multi-hop question answering remains difficult . iterative pipelines must control what to retrieve next and when evidence is adequate. |
| Approach: | They propose an iterative framework with an explicit controller, S2G-Judge . they map structured gap items into the next retrieval query to produce stable retrieval trajectories . |
| Outcome: | Experiments on TriviaQA, HotpotQA, and 2WikiMultiHopQA show that S2G-RAG improves multi-hop QA performance and robustness under multi-turn retrieval. |