Papers with alignment quality
Exploring Hyperbolic Hierarchical Structure for Multimodal Rumor Detection (2025.findings-emnlp)
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| Challenge: | rumor detection models often assume a simplistic one-to-one alignment between modalities . authors present a method that preserves hierarchical, non-linear relationships . |
| Approach: | They propose a method that uses hyperbolic geometry to preserve hierarchical relationships . it decomposes image and text content into three levels and embeds them in hyperbolical space . |
| Outcome: | The proposed method preserves hierarchical relationships rather than representing them at a flat semantic level. |
Monolingual Phrase Alignment as Parse Forest Mapping (2023.starsem-1)
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| Challenge: | Existing methods for phrase alignment are based on unordered tree mapping . syntactic ambiguities can affect alignment quality, so we expand it to parse forests instead of 1-best trees. |
| Approach: | They propose to expand existing method to align parse forests rather than 1-best trees, where syntactic structures and phrase alignment are simultaneously identified. |
| Outcome: | The proposed method improves the state-of-the-art method by aligning forests rather than 1-best trees. |
A Hybrid Approach for Automatic Extraction of Bilingual Multiword Expressions from Parallel Corpora (L18-1)
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| Challenge: | Specific-domain bilingual lexicons are composed of MultiWord Expressions (MWEs) the manual construction of MWEs bilingual dictionaries is costly and time-consuming. |
| Approach: | They propose to use word alignment approaches to automatically construct bilingual lexicons of MWEs from parallel corpora by formalizing the alignment process as an integer linear programming problem. |
| Outcome: | The proposed approach extracts and aligns multiword expressions from parallel corpora and then filters them using linguistic patterns to build bilingual lexicons. |
GAMED.AI: A Hierarchical Multi-Agent Framework for Automated Educational Game Generation (2026.acl-demo)
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| Challenge: | GameDAI is a hierarchical multi-agent framework that transforms instructor-provided questions into fully playable, pedagogically grounded educational games validated through formal mechanic contracts. |
| Approach: | They propose a hierarchical multi-agent framework that transforms instructor-provided questions into fully playable, pedagogically grounded educational games validated through formal mechanic contracts. |
| Outcome: | The proposed framework achieves 90% validation pass rate, 98.3% schema compliance, and 73% token reduction over ReAct agents at 0.46 per game. |
Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model (2021.findings-emnlp)
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| Challenge: | Dot-product attention only considers the pair-wise correlation between words, resulting in dispersion when dealing with long sentences and neglecting source neighboring relationships. |
| Approach: | They propose to model concentrated attention in cross-attention using a Gaussian Mixture Model to model cross- attention in a language model. |
| Outcome: | Experiments on three datasets show that the proposed method outperforms the baseline and has significant improvement on alignment quality, N-gram accuracy, and long sentence translation. |
Word Alignment by Fine-tuning Embeddings on Parallel Corpora (2021.eacl-main)
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| Challenge: | Existing work on word alignment has focused on unsupervised learning on parallel text. |
| Approach: | They propose to combine pre-trained contextualized word embeddings with multilingually trained language models to achieve competitive results on word alignment tasks. |
| Outcome: | The proposed model outperforms state-of-the-art models on five language pairs and can train multilingual word aligners that can obtain robust performance on different language pairs. |
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks (2026.findings-acl)
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Yuanze Hu, Xinyu Wang, Zhichao Yang, Gen Li, Ye Qiu, Zhaoxin Fan, Yifan Sun, Wenjun wu, Jin Dong, Xiaotie Deng
| Challenge: | Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. |
| Approach: | They propose a framework that retrieves context from a memory bank to enhance alignment . they propose EMI-based approach to align vision and language models . |
| Outcome: | The proposed framework reduces training loss, accelerates convergence, and enhances task performance with negligible computational overhead. |
XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments (2025.findings-emnlp)
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| Challenge: | XTRA aims to uncover shared semantic themes across languages . previous methods have achieved improvements in topic diversity but struggle to ensure high topic coherence and consistent alignment across languages. |
| Approach: | a new framework unifies Bag-of-Words modeling with multilingual embeddings is proposed to address this problem . XTRA introduces two core components: (1) representation alignment and (2) topic alignment to enforce cross-lingual consistency. |
| Outcome: | XTRA outperforms baselines in topic coherence, diversity, and alignment quality on multilingual corpora. |
Omni-RewardBench: Toward a Comprehensive Evaluation of Generative Reward Models Across Modalities (2026.acl-long)
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Chi-Min Chan, Yujin Zhou, Pengcheng Wen, Boqin Yin, Jiaming Ji, Juntao Dai, Wei Xue, Sirui Han, Yike Guo
| Challenge: | Existing evaluation benchmarks for ORMs are largely text-centric or limited to bimodal tasks . a new study examines the effectiveness of Omni-RewardBench for ORms across modalities . |
| Approach: | They propose a hybrid automatic-annotation and human-verification pipeline to construct high-quality evaluation data. |
| Outcome: | The proposed model is the first benchmark for comprehensive evaluation of ORMs across modalities. |
DiffPO: Diffusion-styled Preference Optimization for Inference Time Alignment of Large Language Models (2025.acl-long)
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Ruizhe Chen, Wenhao Chai, Zhifei Yang, Xiaotian Zhang, Ziyang Wang, Tony Quek, Joey Tianyi Zhou, Soujanya Poria, Zuozhu Liu
| Challenge: | Inference-time alignment approaches still face limitations due to policy-specific value functions and latency during the inference phase. |
| Approach: | They propose an efficient and policy-agnostic preference optimization method that avoids time latency associated with token generation. |
| Outcome: | The proposed method achieves a favorable trade-off between alignment quality and inference-time latency. |
Curiosity-Driven Reinforcement Learning from Human Feedback (2025.acl-long)
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| Challenge: | Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models with human preferences, but often at the cost of reduced output diversity. |
| Approach: | They propose a framework that incorporates intrinsic rewards for novel states alongside traditional sparse extrinsic rewards to optimize both output diversity and alignment quality. |
| Outcome: | The proposed framework achieves significant gains in diversity on multiple diversity-oriented metrics while maintaining alignment with human preferences comparable to standard RLHF. |
SpeechAlign: A Framework for Speech Translation Alignment Evaluation (2024.lrec-main)
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| Challenge: | Speech-to-Speech and Speech- to-Text translation are currently dynamic areas of research. |
| Approach: | They propose a framework to evaluate source-target alignment in speech models . they introduce a speech gold alignment dataset and introduce two new metrics . |
| Outcome: | The proposed framework evaluates source-target alignment quality within speech models. |
Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning (2026.findings-acl)
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Zhenpeng Su, Leiyu Pan, Minxuan Lv, Tiehua Mei, Zijia Lin, Yuntao Li, Wenping Hu, Ruiming Tang, Kun Gai, Guorui Zhou
| Challenge: | Large language model post-training often adopts an off-policy training paradigm . however, the off-poliicy training model introduces distribution shifts that push the policy beyond the trust region. |
| Approach: | They propose to use the entropy ratio as a global metric to measure the relative change in policy exploration throughout updates. |
| Outcome: | Experiments show that the proposed metric improves performance across multiple benchmarks. |
ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework (2026.acl-long)
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Kai Qin, Liangxin Liu, Yu Liang, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Houde Liu, Daiting Shi
| Challenge: | Existing methods for generating reward models focus on outcome-level supervision, neglecting analytical process quality, which constrains their potential. |
| Approach: | They propose a novel reward model that leverages self-reflection to assess analytical quality and enhance preference modeling. |
| Outcome: | The proposed model improves performance on four benchmarks and significantly mitigates positional bias. |