Papers with Autoregressive
Non-Autoregressive Models for Fast Sequence Generation (2022.emnlp-tutorials)
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| Challenge: | Autoregressive (AR) models can only generate target sequence word-by-word due to the AR mechanism and suffer from slow inference. |
| Approach: | This tutorial provides an introduction to non-autoregressive sequence generation. |
| Outcome: | This tutorial explains how to generate non-autoregressive sequence generation models. |
C2DLM: Causal Concept-Guided Diffusion Large Language Models (2026.findings-acl)
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Kairong Han, Nuanqiao Shan, Ziyu Zhao, Zijing Hu, Xinpeng Dong, Ye Jun Jian, Lujia Pan, Fei Wu, Kun Kuang
| Challenge: | Autoregressive (AR) and diffusion language models (DLMs) suffer from insufficient reasoning capabilities. |
| Approach: | They propose a fully connected Diffusion Language Model that uses a concept-level causal graph to guide attention to learn causal relationships between concepts. |
| Outcome: | The proposed model achieves a 12% improvement and 3.2 training speedup on the COT-OrderPerturb task, along with an average gain of 1.31% across six downstream reasoning tasks. |
Guided by the Plan: Enhancing Faithful Autoregressive Text-to-Audio Generation with Guided Decoding (2026.eacl-long)
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| Challenge: | Autoregressive (AR) models excel at generating temporally coherent audio by producing tokens sequentially, yet they often falter in faithfully following complex textual prompts. |
| Approach: | They propose a lightweight auxiliary model trained with a GAE-inspired objective to predict final instruction-following quality from partial generations. |
| Outcome: | The proposed model achieves 10 points improvement in CLAP score over baseline AR models while maintaining computational parity with best-of-N decoding. |
Incremental Sentence Processing Mechanisms in Autoregressive Transformer Language Models (2025.naacl-long)
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| Challenge: | a recent study has found LMs focus on processing syntactic relations, but not structural information. |
| Approach: | They examine the mechanisms underlying garden path sentence processing in LMs . they use sparse autoencoders to identify interpretable features that determine which continuation . |
| Outcome: | The proposed model lacks syntactic features and shallow heuristics to perform incremental sentence processing. |
DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) large audio language models are expensive in data and computation . prior work shows diffusion-based LALMs can improve audio understanding under matched settings . |
| Approach: | They propose a diffusion-based LALM that upgrades the speech encoder and employs dual semantic and acoustic adapters. |
| Outcome: | a new model improves over existing autoregressive large language models and is competitive to strong AR models . the proposed model can make use of limited training data and improve inference efficiency . a recent study shows that diffusion-based models can improve audio understanding . |
Autoregressive Text Generation Beyond Feedback Loops (D19-1)
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| Challenge: | Autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies. |
| Approach: | They combine a latent state space model with a CRF observation model to investigate the state evolution of a hidden state trajectory. |
| Outcome: | The proposed model performs better on unconditional sentence generation compared to baselines while avoiding some prototypical failure modes. |
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation (2021.findings-acl)
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| Challenge: | Existing methods for table-to-text generation suffer from poor faithfulness and low coverage. |
| Approach: | They propose a method that combines Autoregressive and Non-Autoregressive generation to generate a table-to-text from a key-value table using a skeleton and an edit-based non-autoregressively generation model. |
| Outcome: | The proposed method outperforms the existing methods on WikiPerson and WikiBio datasets on coverage and faithfulness. |
Universal Conditional Masked Language Pre-training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Pre-trained sequence-to-sequence models have significantly improved Neural Machine Translation (NMT) this paper demonstrates that pre-training a sequence- to-squence model with a bidirectional decoder can produce notable performance gains for both Autoregressive and Non-autoregressive NMT tasks. |
| Approach: | They propose a conditional masked language model pre-trained on bilingual and monolingual corpora in many languages. |
| Outcome: | The proposed model can achieve significant performance improvements on all scenarios from low- to extremely high-resource languages. |
Robust and Unbounded Length Generalization in Autoregressive Transformer-Based Text-to-Speech (2025.naacl-long)
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Eric Battenberg, RJ Skerry-Ryan, Daisy Stanton, Soroosh Mariooryad, Matt Shannon, Julian Salazar, David Teh-Hwa Kao
| Challenge: | Autoregressive (AR) Transformer-based sequence models have difficulty generalizing to sequences longer than those seen during training. |
| Approach: | They propose a system that provides cross-attention operations with relative location information. |
| Outcome: | The proposed system matches the naturalness and expressiveness of a baseline T5-based system while eliminating problems with repeated or dropped words. |
Treepiece: Faster Semantic Parsing via Tree Tokenization (2023.findings-emnlp)
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| Challenge: | Autoregressive (AR) encoder-decoder neural networks are slow in sequential prediction of natural language to machine-readable parse trees. |
| Approach: | They propose a technique that tokenizes a parse tree into subtrees and generates one subtrea per decoding step. |
| Outcome: | The proposed approach shows 4.6 times faster decoding speed and comparable speed but significantly higher accuracy compared to non-autoregressive (NAR) models. |
From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons (2026.acl-long)
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| Challenge: | Autoregressive (AR) models rely on bidirectional attention, creating a structural mismatch with pre-trained Autoregression models. |
| Approach: | They propose a framework that efficiently adapts autoregressive (AR) models to the diffusion paradigm. |
| Outcome: | The proposed framework reduces training costs by orders of magnitude while maintaining state-of-the-art performance. |
DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) decoding in large language models is latency-bounded by strictly sequential token generation. |
| Approach: | They propose a diffusion-based drafter that proposes multi-token candidates and then verifies them in parallel by the target model. |
| Outcome: | The proposed drafter generates multi-token proposals in a single forward pass while remaining compatible with standard AR verifiers. |
SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation (2026.findings-acl)
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Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou
| Challenge: | Autoregressive (AR) language models are a dominant paradigm in the field of parallelism and non-causal modeling. |
| Approach: | They propose a blockwise discrete diffusion model that preserves AR-compatible serving while enabling parallel intra-block generation. |
| Outcome: | The proposed model achieves theoretical speedups over 5 and wall-clock speedup of 2.3 on H200 GPUs in latency-critical regimes. |
Visual Self-Refinement for Autoregressive Models (2025.findings-emnlp)
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Jiamian Wang, Ziqi Zhou, Chaithanya Kumar Mummadi, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Chen Qiu, Zhiqiang Tao
| Challenge: | Autoregressive models excel in sequential modeling but the spatial nature of visual signals conflicts with the sequential dependencies of next-token prediction, leading to suboptimal results. |
| Approach: | They propose a plug-and-play refinement module to enhance the spatial correspondence modeling within the generated visual sequence. |
| Outcome: | The proposed module enhances vision-language modeling under a shared sequential prediction framework. |