Papers with TTA
ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling (2026.acl-long)
Copied to clipboard
| Challenge: | Recent efforts on text-to-audio generation are exploring fine-grained controllability . however, their performance at scale is limited due to data scarcity . |
| Approach: | They propose a multi-task learning problem for high-controllability text-to-audio generation . they propose scalable diffusion transformers that augment condition information in sequence . |
| Outcome: | The proposed method outperforms existing methods on objective and subjective evaluations. |
LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for post-training quantization (PTQ) are limited by the complexity of the quantization parameter and performance degradations when tested on unseen datasets. |
| Approach: | They propose a learnable smooth-based PTQ framework that allows for rapid adaptation during testing. |
| Outcome: | The proposed framework improves performance on unseen datasets and reduces memory constraints. |
RiTTA: Modeling Event Relations in Text-to-Audio Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing text-to-audio (TTA) generation methods have not explored audio event relation modeling, nor proposed any new framework to enhance this capability. |
| Approach: | They propose a comprehensive relation corpus covering all potential relations in real-world scenarios and a new audio event corpus encompassing commonly heard audios. |
| Outcome: | The proposed framework improves existing models’ relation modeling capability with negligible extra parameters. |
CTTA-T: Continual Test-Time Adaptation for Text Understanding via Teacher-Student with a Domain-aware and Generalized Teacher (2026.acl-long)
Copied to clipboard
Tianlun Liu, Zhiliang Tian, Zhen Huang, Xingzhi Zhou, Wanlong Yu, Tianle Liu, Feng Liu, Dongsheng Li
| Challenge: | Existing models for text understanding fail to adapt to domain shifts in real-world applications . current models do not improve themselves as they are applied to new domains . |
| Approach: | They propose a continual test-time adaptation framework that adapts to evolving domains . they propose accumulating domains and a refine-then-filter framework to calibrate teacher predictions . |
| Outcome: | The proposed model excels in a teacher-student framework adaptable to evolving domains. |
Test-time Augmentation for Factual Probing (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to improve factual probing are relation-specific and do not generalize to unseen relation types. |
| Approach: | They propose to use test-time augmentation to augment and ensemble prompts at test time to reduce sensitivity to prompt variations. |
| Outcome: | The proposed method improves model confidence, but for other models, it leads to degradation. |
Effective Large Language Model Adaptation for Improved Grounding and Citation Generation (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models generate "hallucinated" answers that are not factual . despite their widespread adoption, they can generate plausiblesounding but nonfactual information. |
| Approach: | They propose a framework that tunes large language models to self-ground claims and provide citations to retrieved documents. |
| Outcome: | The proposed framework generates superior grounded responses with more accurate citations compared to prompting-based approaches and post-hoc citing-based methods. |
Generative-to-Discriminative Test-Time Adaptation via Manifold-Aware Diffusion and Bayesian Distillation (2026.findings-acl)
Copied to clipboard
Boyun Zhang, Zequn Xie, Fangming Feng, Zihan Zhang, Yongbo He, Chuxin Wang, Sihang Cai, Tao Jin, Qifei Zhang
| Challenge: | Existing discriminative approaches suffer from "confident but wrong" failure mode, blindly adapting to OOD noise leading to error accumulation. |
| Approach: | They propose a TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD). |
| Outcome: | The proposed framework reduces MAE from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%) it also reduces the MAE of the MOSI to SIMS shift and achieves an 11.18-point gain over the baseline. |
Advancing Test-Time Adaptation in Wild Acoustic Test Settings (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing wild vision TTA methods fail to handle speech data due to the unique characteristics of high-entropy speech frames, which are unreliably filtered out even when containing crucial semantic content. |
| Approach: | They propose a method for acoustic foundation models to perform confidence-based adaptation in wild acustic test settings. |
| Outcome: | The proposed method outperforms baselines under Gaussian noise, environmental sounds, accent variations, and sung speech in the wild. |
Cross-domain Rumor Detection via Test-Time Adaptation and Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches focus on within-domain tasks, resulting in suboptimal performance in cross-domain scenarios due to domain shifts. |
| Approach: | They propose a framework that incorporates both single-domain model and target graph adaptation strategies tailored to the unique requirements of cross-domain rumor detection. |
| Outcome: | The proposed framework surpasses existing methods in rumor detection on social media. |
Robust Question Answering against Distribution Shifts with Test-Time Adaption: An Empirical Study (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on robustness tuning (RT) methods has found that QA models fail when the test data has a distribution shift compared to the training data. |
| Approach: | They propose to use test-time adaptation methods to improve QA models after deployment to evaluate their model against text corruption and changes in language and domain. |
| Outcome: | The proposed method improves TTA to be more robust to variation in hyper-parameters and test distributions over time. |
MeanAudio: Fast and Faithful Text-to-Audio Generation with Mean Flows (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances in Text-to-Audio Generation (TTA) systems suffer from slow inference speed, authors report . authors demonstrate that MeanAudia achieves state-of-the-art performance in single-step audio generation . |
| Approach: | They propose a text-to-audio generator capable of rendering realistic sound with only one function evaluation. |
| Outcome: | The proposed system achieves state-of-the-art performance in single-step audio generation. |
Beware of Model Collapse! Fast and Stable Test-time Adaptation for Robust Question Answering (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have achieved great success in question answering, but their robustness is insufficient to support their practical applications. |
| Approach: | They propose a method which regularizes the model's output and an efficient side block to reduce its inference time. |
| Outcome: | The proposed method achieves comparable or better results than previous TTA methods at a speed close to vanilla forward propagation, which is 1.8 to 4.4 speedup compared to previous methods. |
Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Debiased large language models excel at handling known or low-bias prompts, but fail on unfamiliar and high-biased prompts. |
| Approach: | They propose a debiasing framework that detects high-bias prompts and triggers context-aware LoRA updates only when a bias-risk score exceeds a threshold. |
| Outcome: | The proposed framework reduces toxicity/bias score with significantly lower latency than standard optimization methods. |
SAME: Signer-Aware Mixture-of-Experts for Test-Time Adaptation in Sign Language Translation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for supervised fine-tuning are limited due to labeled data . existing methods require long adaptation times and batch statistics are unavailable in streaming settings . |
| Approach: | They propose a plug-and-play, signer-aware Mixture-of-Experts (MoE) TTA architecture for SLT . they use a combination of lightweight MoE modules and unsupervised regularizers to decouple domain shift . |
| Outcome: | The proposed test-time adaptation outperforms existing TTA methods in sign language translation . the proposed architecture can be used in real-world deployments without labeling . |
Dynamic Model-Bank Test-Time Adaptation for Automatic Speech Recognition (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing ASR TTA methods struggle with instability under continual and long-term distribution shifts. |
| Approach: | They propose a continuous adaptive model-bank framework that adapts to domain shifts in ASR test-time scenarios. |
| Outcome: | Experiments on diverse, continuously shifting ASR benchmarks show that DMSUTA outperforms existing continual TTA baselines. |
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current ASR TTA methods focus on non-continual TTA, which limits cross-sample knowledge learning compared to continual TTA. |
| Approach: | They propose a Fast-slow TTA framework that leverages the advantage of continual and non-continual TTA and a Dynamic SUTA method that automatically detects domain shifts and resets the model. |
| Outcome: | The proposed method outperforms non-continual and continual TTA methods while maintaining robustness to domain shifts without requiring domain boundary information. |
UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions (2026.acl-long)
Copied to clipboard
Chunyu Qiang, Xiaopeng Wang, Kang Yin, Yuzhe Liang, Yuxin Guo, Teng Ma, Ziyu Zhang, Tianrui Wang, Cheng Gong, Yushen Chen, Ruibo Fu, Longbiao Wang, Jianwu Dang
| Challenge: | Generative audio modeling has been fragmented into specialized tasks such as text-to-speech (TTS), text- to-music (TTM), and text-ta (TTA) specialized models require reference audio for timbre cloning and strict phoneme alignment, whereas TTA models generate unstructured textures from open-ended captions. |
| Approach: | They propose a unified flow-matching framework capable of synthesizing speech, music, sound effects . they propose 'token injection mechanism' that projects unstructured environmental sounds into structured temporal latent space . |
| Outcome: | The proposed framework achieves state-of-the-art performance in instruction-based TTS and TTM while maintaining competitive fidelity in TTA. |
STAF: Pushing the Boundaries of Test-Time Adaptation towards Practical Noise Scenarios (2024.lrec-main)
Copied to clipboard
| Challenge: | Pre-trained language models have demonstrated superior performance on NLP tasks . however, when the training domain and testing domain are taken from different distributions, the deployed model often violates this assumption. |
| Approach: | They propose a Stable Test-time Adaptation Framework to stabilize the adaptation process. |
| Outcome: | The proposed framework boosts model robustness to noise distribution shifts while minimizing error accumulation and catastrophic forgetting. |
Training-Free Test-Time Contrastive Learning for Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing training-free alternatives to training-based models are static or depend on external guidance. |
| Approach: | They propose a training-free adaptation framework that enables a frozen LLM to improve online by distilling supervision from its own inference experiences. |
| Outcome: | The proposed framework outperforms existing test-time adaptation methods under online evaluation. |