Papers by Rui Hao

Rethinking Diverse Human Preference Learning through Principal Component Analysis (2025.findings-acl)

Challenge:	Existing methods for code retrieval struggle to balance scalability and annotation quality.
Approach:	They propose a method that integrates functions called within the repository and information on third-party APIs to enhance the annotation context.
Outcome:	The proposed method improves the annotation context by incorporating functions called within the repository and information on third-party API functionalities.

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Feng Luo, Rui Yang, Hao Sun, Chunyuan Deng, Jiarui Yao, Jingyan Shen, Huan Zhang, Hanjie Chen

Explainability & Interpretability in NLP

Challenge:	Decomposed Reward Models extract diverse human preferences from binary comparisons without fine-grained annotations.
Approach:	They propose a decomposed reward model that extracts diverse human preferences from binary comparisons without fine-grained annotations.
Outcome:	The proposed approach extracts diverse human preferences from binary comparisons without fine-grained annotations.

DynamicFocalPO: Adaptive Focusing Strategy for Preference Optimization (2026.findings-acl)

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Shu Zhou, Junan Chen, Rui Ling, Xin Wang, Tao Fan, Hao Wang

Enhancing Extractive Question Answering in Multiparty Dialogues with Logical Inference Memory Network (2025.coling-main)

Challenge:	Recent preference optimization algorithms such as Direct Preference Optimization (DPO) have become prevalent for aligning large language models with human preferences.
Approach:	They propose a preference optimization algorithm that introduces a modulating factor that down-weighs misranked preference pairs and employs focusing strategy that adapts over the course of training.
Outcome:	Experiments show that DynamicFocalPO surpasses both DPO and FocalPO on benchmarks including Alpaca Eval 2.0 and Arena-Hard using Mistral-Base-7B and Llama-3-Instruct-8B.

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Shu Zhou, Rui Zhao, Zhengda Zhou, Haohan Yi, Xuhui Zheng, Hao Wang

Natural Language Interfaces

Challenge:	Existing models for multiparty dialogue question answering (QA) do not consider logical inference relations in multiparty dialogs, leading to suboptimal performance.
Approach:	They propose a memory network with logical inference for extractive QA in multiparty dialogues.
Outcome:	The proposed model achieves state-of-the-art on Molweni and FriendsQA benchmarks.

Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval (2026.acl-long)

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Hao Xu, Rite Bo, Fausto Giunchiglia, Yingji Li, Rui Song

ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors (2024.findings-emnlp)

Challenge:	Large language models (LLMs) perform well on Out-of-Distribution tasks, but performance degrades as distribution shift becomes more severe.
Approach:	They propose a demonstration search framework that incorporates an OOD proxy to approximate the inaccessible target domain and guide the retrieval process.
Outcome:	The proposed framework enhances robustness in out-of-distribution tasks by incorporating an OOD proxy to approximate the inaccessible target domain and guide the retrieval process.

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Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, Minlie Huang

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)

Challenge:	Existing tools for detecting safety issues in LLMs are expensive and inefficient.
Approach:	They propose an LLM-based safety detector which annotates the safety of queries and provides explanations for its decisions.
Outcome:	The proposed detector outperforms baselines on four sets of query-response pairs and is effective as a safety evaluator for advanced LLMs.

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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou

CLEAN–EVAL: Clean Evaluation on Contaminated Large Language Models (2024.findings-naacl)

Challenge:	Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Approach:	They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn .
Outcome:	The proposed benchmark is very challenging for state-of-the-art models, it is found.

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Wenhong Zhu, Hongkun Hao, Zhiwei He, Yun-Ze Song, Jiao Yueyang, Yumeng Zhang, Hanxu Hu, Yiran Wei, Rui Wang, Hongyuan Lu

To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)

Challenge:	Existing methods to evaluate large language models are prone to data contamination.
Approach:	They propose a method which parses contaminated data and back-translates it into a candidate set.
Outcome:	The proposed method reduces data contamination and evaluates the LLMs more cleanly.

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Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie

Loki: An Open-Source Tool for Fact Verification (2025.coling-demos)

Challenge:	Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy.
Approach:	They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction.
Outcome:	The proposed model improves generalization ability and makes distant link prediction significantly easier.

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Haonan Li, Xudong Han, Hao Wang, Yuxia Wang, Minghan Wang, Rui Xing, Yilin Geng, Zenan Zhai, Preslav Nakov, Timothy Baldwin

A Joint Learning Framework for Restaurant Survival Prediction and Explanation (2022.emnlp-main)

Challenge:	Loki is an open-source fact-checking tool designed to address the growing problem of misinformation.
Approach:	They propose a tool that breaks down the fact-checking task into five steps . they propose LOKI, which offers a semiautomated, human-in-the-loop approach .
Outcome:	a new open-source tool is designed to address the growing problem of misinformation . the tool breaks down the fact-checking task into five steps to assist human judgment .

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Xin Li, Xiaojie Zhang, Peng JiaHao, Rui Mao, Mingyang Zhou, Xing Xie, Hao Liao

Explainability & Interpretability in NLP

Challenge:	Recent advances in deep learning have various models that research reviews and interactions for different kinds of tasks, such as predicting restaurant survival.
Approach:	They propose a joint learning framework for explainable restaurant survival prediction based on multi-modal data of user-restaurant interactions and users’ textual reviews.
Outcome:	The proposed framework improves on two datasets showing that it can model restaurant interactions and users’ textual reviews.

Vision-Enhanced Semantic Entity Recognition in Document Images via Visually-Asymmetric Consistency Learning (2023.emnlp-main)

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Hao Wang, Xiahua Chen, Rui Wang, Chenhui Chu

Visual Data in NLP

Challenge:	Existing models train a visual encoder with weak cross-modal supervision signals, resulting in a limited capacity to capture non-textual features and suboptimal performance.
Approach:	They propose a Visually-Asymmetric coNsistenCy Learning approach that enhances the model’s ability to capture fine-grained visual and layout features through the incorporation of color priors.
Outcome:	The proposed approach outperforms the strong LayoutLM series baseline on benchmark datasets and provides insights for optimizing model performance.

RePair: Automated Program Repair with Process-based Feedback (2024.findings-acl)

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Yuze Zhao, Zhenya Huang, Yixiao Ma, Rui Li, Kai Zhang, Hao Jiang, Qi Liu, Linbo Zhu, Yu Su

Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View (2026.findings-acl)

Challenge:	Commercial-scale language models (LMs) have taken APR to unprecedented levels, but they are limited by parameters and humans interact with them through explicit prompts.
Approach:	They propose a method that utilizes process supervision to improve program repair by allowing users to input feedback from compilers and test cases.
Outcome:	The proposed method outperforms large outcome-based generation methods and is inspired by strategies used in programming competitions.

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Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, null Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, Rui Mao

Information Retrieval

Challenge:	Existing approaches to reduce OOD recommendations fall into three grounding paradigms: retrieval, constrained generation and discrete item tokenizer generation.
Approach:	They propose a framework that instantiates three grounding paradigms under a single architecture . embedding-based retrieval, constrained generation and discrete item-tokenizer methods are implemented .
Outcome:	The proposed framework eradicates OOD recommendations across all variants and achieves state-of-the-art accuracy compared to strong ID-based and LLM-based baselines.

A Cognitive Writing Perspective for Constrained Long-Form Text Generation (2025.findings-acl)

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Kaiyang Wan, Honglin Mu, Rui Hao, Haoran Luo, Tianle Gu, Xiuying Chen

Text Generation

Challenge:	Large Language Models struggle to generate high-quality long-form text in a single pass . a new framework that trains LLMs to write human-like writing capabilities is needed .
Approach:	They propose a framework that equips large language models with human-like cognitive writing capabilities . they use a planning agent and multiple Generation Agents to generate long-form text in parallel .
Outcome:	CogWriter surpasses GPT-4o by 22% in complex instruction completion accuracy . the framework can generate coherent text in a single pass with fluency that rivals human writers .

A Dual-Mind Framework for Strategic and Expressive Negotiation Agent (2025.acl-long)

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Yutong Liu, Lida Shi, Rui Song, Hao Xu

Challenge:	Existing approaches to negotiation dialogue focus on only one aspect, ignoring the synergistic effect of their combined synergies.
Approach:	They propose a dual-mind negotiation agent framework that integrates an intuitive and a deliberative module for slow, expression optimization.
Outcome:	The proposed framework achieves state-of-the-art on negotiation datasets showing that it improves negotiation ability.

Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning (2026.acl-long)

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Rui Song, Lida Shi, Ruihua Qi, Yingji Li, Hao Xu

Penalty Decoding: Well Suppress the Self-Reinforcement Effect in Open-Ended Text Generation (2023.emnlp-main)

Challenge:	Existing studies have explored the evolutionary analysis of ancient scripts, with particular attention to the transformation of character forms from oracle bone inscriptions to regular script.
Approach:	They propose a benchmark framework that leverages MLLMs to analyze the evolution of ancient Chinese scripts.
Outcome:	The proposed framework improves performance on core tasks and character recognition and evolutionary reasoning tasks while limiting performance on other tasks.

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Wenhong Zhu, Hongkun Hao, Rui Wang

Text Generation

Challenge:	Experimental results demonstrate the efficacy of our approach in generating high-quality sentences resembling human output.
Approach:	They propose a forgetting mechanism that disregards distant tokens, reducing the burden of penalty selection.
Outcome:	The proposed approach generates high-quality sentences resembling human output.

BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation (2026.acl-demo)

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Yang Qi, Xiangyao Ma, Xiao Wang, Hao Wang, Rui Wang

Multilinguality

Challenge:	Existing document translation pipelines face a tension between linguistic processing and layout preservation.
Approach:	They propose a framework for layout-preserving PDF translation that decouples visual layout metadata from semantic content.
Outcome:	The proposed framework improves layout fidelity, visual aesthetics, and terminology consistency over representative baselines while maintaining competitive translation precision.

Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification (2021.emnlp-main)

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Hao Chen, Rui Xia, Jianfei Yu

Information Retrieval

Challenge:	Existing approaches to improve generalization ability by augmenting training data with synonymous examples or adding random noises to word embeddings cannot address spurious association problem.
Approach:	They propose an end-to-end reinforcement learning framework which jointly performs counterfactual data generation and dual sentiment classification.
Outcome:	The proposed framework outperforms strong data augmentation baselines on several benchmark sentiment classification datasets.

CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs (2026.findings-acl)

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Xingcheng Zhou, Hao Guo, Rui Song, Walter Zimmer, Mingyu Liu, André Schamschurko, Hu Cao, Alois Knoll

Multimodality

Challenge:	Existing Vision-language models are prone to hallucinating nonexistent entities or events and missing subtle but critical visual cues.
Approach:	They propose a Traffic VideoQA Benchmark that enforces a single structured decision pattern over each video question quadruple and provides actionable diagnostics that decompose failures into positive omission, positive swap, negative hallucination, mutual-exclusivity violation.
Outcome:	The proposed model detects true hazards when an accident occurs, and rejects plausible-but-false hypotheses under near-identical counterfactual scenes.

From Fake to Real: Mitigating Out-of-Distribution Bias in In-Context Learning via Feedback Supervision from Large Language Models (2026.findings-acl)

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Rui Song, Yingji Li, Jian Li, Fausto Giunchiglia, Hao Xu

UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning (2022.acl-long)

Challenge:	In-Context Learning (ICL) is one of the most common methods for complex Natural Language Understanding tasks.
Approach:	They propose a method that uses model confidence and perturbation perplexity to enhance the quality of pseudo-labels.
Outcome:	The proposed method reduces OOD biases by avoiding direct use of source data.

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Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Scott Yih, Madian Khabsa

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment (2025.emnlp-main)

Challenge:	Existing methods for parameter-efficient language model tuning (PELT) match the performance of fine-tuning with fewer trainable parameters.
Approach:	They propose a framework which integrates different PELT methods as submodules and learns to activate the ones that best suit the current data or task setup via gating mechanism.
Outcome:	The proposed framework outperforms fine-tuning methods on the GLUE benchmark and achieves 14% gains over the best individual PELT method.

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Hao Li, Lijun Li, Zhenghao Lu, Xianyi Wei, Rui Li, Jing Shao, Lei Sha

A Simple Contrastive Learning Framework for Interactive Argument Pair Identification via Argument-Context Extraction (2022.emnlp-main)

Challenge:	Recent studies show that fine-tuning with benign data can compromise safety of aligned LLMs.
Approach:	They propose a Layer-Aware Representation Filtering method that detects safety-degrading layers within the LLM and leverages their representations to detect them.
Outcome:	The proposed method can detect safety-degrading features in benign data and remove them from the model.

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Lida Shi, Fausto Giunchiglia, Rui Song, Daqian Shi, Tongtong Liu, Xiaolei Diao, Hao Xu

Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents (2024.lrec-main)

Challenge:	Existing work on argument mining uses context-based methods to identify whether two arguments are interactively related.
Approach:	They propose a contrastive learning framework to extract valuable information from the context.
Outcome:	The proposed framework achieves state-of-the-art performance on the benchmark dataset and visually displays more compact representations.

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Hao Wang, Tang Li, Chenhui Chu, Rui Wang, Pinpin Zhu

Multimodality

Challenge:	Existing document AI approaches fail to consider key-value relations in visually-rich documents . a few-shot approach is proposed to extract key- value relation triplets in VRDs .
Approach:	They propose a few-shot relational learning approach targeting the extraction of key-value relation triplets in Visually-Rich Documents.
Outcome:	The proposed method outperforms existing methods in visually-rich documents.

Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints (2022.naacl-main)

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Chun Zeng, Jiangjie Chen, Tianyi Zhuang, Rui Xu, Hao Yang, Qin Ying, Shimin Tao, Yanghua Xiao

Machine Translation

Challenge:	Existing approaches to lexically constrained neural machine translation suffer from high latency.
Approach:	They propose a plug-in algorithm for non-autoregressive translation for this problem . they propose ACT to familiarize the model with the source-side context of constraints .
Outcome:	The proposed model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints.

Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement (2026.acl-long)

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Qimin Zhong, Hao Liao, Haiming Qin, Mingyang Zhou, Rui Mao, Wei Chen, Naipeng Chao

Effective Long-Context Scaling of Foundation Models (2024.naacl-long)

Challenge:	Existing methods to learn internal world models rely on one-step supervision . however, standard MTP suffers from structural hallucinations .
Approach:	They propose a method which anchors predictions to ground-truth hidden state trajectories.
Outcome:	The proposed method bridges the gap between discrete tokens and continuous state representations, reducing structural hallucinations, and improving robustness to perturbations.

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Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, Madian Khabsa, Han Fang, Yashar Mehdad, Sharan Narang, Kshitiz Malik, Angela Fan, Shruti Bhosale, Sergey Edunov, Mike Lewis, Sinong Wang, Hao Ma

ROSE: Robust Selective Fine-tuning for Pre-trained Language Models (2022.emnlp-main)

Challenge:	Large language models (LLMs) are rapidly deployed and continue to evolve through scaling.
Approach:	They propose a method to train strong long-context LLMs that are capable of utilizing massive context windows of up to 32,000 tokens.
Outcome:	The proposed model can surpass gpt-3.5-turbo-16k's overall performance on long-context benchmarks with a cost-effective instruction tuning procedure that is free of expensive annotations.

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Lan Jiang, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Rui Jiang

Robustness in NLP

Challenge:	Recent studies have highlighted the lack of adversarial robustness in pre-trained models.
Approach:	They propose a fine-tuning approach that conducts selective updates when adapting pre-trained models to downstream tasks.
Outcome:	The proposed approach improves adversarial robustness on downstream tasks . it eliminates spurious updates, leading to flatter and wider optima than the conventional method .

DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine Reading (2023.findings-emnlp)

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Hao Wang, Qingxuan Wang, Yue Li, Changqing Wang, Chenhui Chu, Rui Wang

Multimodality

Challenge:	Document AI models that can read visually rich documents have a long way to go before they can read them as accurately, continuously, and flexibly as humans do.
Approach:	They propose a visually-rich document dataset that aligns with human eye-movement information using eye-tracking technology.
Outcome:	The proposed dataset can help in designing better document AI models and human reading robots in the future.

Hierarchical Reward Modeling for Fault Localization in Large Code Repositories (2025.findings-emnlp)

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Jiwei Zhang, Jianxun Lian, Haiming Qin, Mingyang Zhou, KeZhong Lu, Rui Mao, Hao Liao

Challenge:	Large Language Models (LLMs) have limited fault localization capabilities due to limited context length.
Approach:	They propose a hierarchical localization reward model to evaluate and select the most accurate fault localization candidates from the outputs of LLMs.
Outcome:	The proposed model improves the final line-level localization recall by 12% on the SWE-Bench-Lite dataset.

GLiM: Integrating Graph Transformer and LLM for Document-Level Biomedical Relation Extraction with Incomplete Labeling (2025.findings-acl)

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Hao Fang, Yuejie Zhang, Rui Feng, Yingwen Wang, Qing Wang, Wen He, Xiaobo Zhang, Tao Zhang, Shang Gao

Information Extraction & Text Mining

Challenge:	Document-level relation extraction (DocRE) solves problems of document quality . number of entities and entity-pair relations increases, causing incomplete annotations .
Approach:	a framework that reduces the problem space using a graph-enhanced Transformer-based model is proposed . GLiM leverages large language models for reasoning to reduce the problem-space .
Outcome:	GLiM boosts average recall and F1 scores on biomedical datasets . compared with existing models, GLim outperforms existing models on biomedicine benchmarks compared to existing models .

Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models (2024.acl-long)

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Zhiwei He, Binglin Zhou, Hongkun Hao, Aiwei Liu, Xing Wang, Zhaopeng Tu, Zhuosheng Zhang, Rui Wang

Multilinguality

Challenge:	Existing text watermarking technologies lack consistency when texts are translated into different languages.
Approach:	They propose a cross-lingual watermark removal attack to bypass watermarking by first obtaining a response from an LLM in a pivot language and then translating it into the target language.
Outcome:	The proposed method can remove watermarks without performance loss by obtaining a response from an LLM in a pivot language and then translating it into the target language.

An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism (2022.acl-long)

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Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, ZongYu Wang, Rui Xie, Wei Wu, Man Lan

Structured Data in NLP

Challenge:	Existing methods focus on graph representation learning, but decoding is a key part of the process.
Approach:	They propose an EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI) they combine two sets of isomorphic equations to enhance the decoding process .
Outcome:	The proposed algorithm can deliver significant performance improvements even on the most advanced methods while the extra required time is less than 3 seconds.

ACSE: An Ancient Character Semantic-Aware Embedding for Large Language Models (2026.findings-acl)

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Zhihan Zhou, Daqian Shi, Lida Shi, Rui Song, Peiqiang Qiu, Xiaolei Diao, Hao Xu

SolEval: Benchmarking Large Language Models for Repository-level Solidity Smart Contract Generation (2025.emnlp-main)

Challenge:	Existing studies on pre-Qin documents are insufficient to understand ancient characters . ancient characters have a low level of digitization and training corpora are extremely scarce .
Approach:	They propose a semantic-aware embedding for ancient Chinese characters that integrates glyphs and lexicality into modern Chinese semantic space.
Outcome:	The proposed model integrates glyph and lexicality of ancient characters and maps them to the modern Chinese semantic space.

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Zhiyuan Peng, Xin Yin, Rui Qian, Peiqin Lin, YongKang Liu, Hao Zhang, Chenhao Ying, Yuan Luo

R-CHAR: A Metacognition-Driven Framework for Role-Playing in Large Language Models (2025.emnlp-main)

Challenge:	Existing methods focus on Python and Java, neglecting Solidity, the programming language for Ethereum smart contracts.
Approach:	They construct a repository-level benchmark for Solidity to evaluate the performance of LLMs on Ethereum.
Outcome:	The proposed benchmarks show that the best performing LLM achieves only 26.29% Pass@10, highlighting room for improvement in Solidity code generation.

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Haiming Qin, Jiwei Zhang, Wei Zhang, KeZhong Lu, Mingyang Zhou, Hao Liao, Rui Mao

Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment (2026.acl-long)

Challenge:	Existing role-playing structures lack cognitive consistency in complex scenarios . Existing models excel in math and coding tasks but lack coherent reasoning .
Approach:	They propose a metacognition-driven framework that enhances role-playing performance . experimental results show performance improvements across varying scenario complexities .
Outcome:	The proposed framework outperforms existing models in social intelligence tasks and shows strength in long-context comprehension and group-level social interactions.

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Bobo Li, Wu Rui, Zibo Ji, Meishan Zhang, Hao Fei, Min Zhang, Mong-Li Lee, Wynne Hsu

Reasoning

Challenge:	Large Language Models (LLMs) have enabled the development of powerful autonomous systems.
Approach:	They propose a model trained through dialectical alignment to enforce perspective-invariant reasoning.
Outcome:	The proposed model mitigates attribution inconsistency and significantly improves fault resolution rates in ambiguous scenarios.

Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree Awareness (2024.findings-emnlp)

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Jian Li, Haojing Huang, Yujia Zhang, Pengfei Xu, Xi Chen, Rui Song, Lida Shi, Jingwen Wang, Hao Xu

IDPG: An Instance-Dependent Prompt Generation Method (2022.naacl-main)

Challenge:	Recent studies have focused on replacing the reward model in Reinforcement Learning with Human Feedback (RLHF) methods for Large Language Models (LLMs).
Approach:	They propose a self-supervised preference optimization framework that replaces the reward model with a preference loss and alignment loss to improve LLMs' ability to understand human preferences.
Outcome:	The proposed framework can be integrated with existing preference optimization methods and significantly boost their performance.

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Zhuofeng Wu, Sinong Wang, Jiatao Gu, Rui Hou, Yuxiao Dong, V.G.Vinod Vydiswaran, Hao Ma