ADAPTATION

Addressing the Vulnerability of NMT in Input Perturbations (2021.naacl-industry)

Challenge:	Recent work on zero-shot visual question answering does not explicitly consider multi-step reasoning chains, making them less interpretable compared with a decomposition-based approach.
Approach:	They propose a modularized zero-shot network that explicitly decomposes questions into sub reasoning steps and is highly interpretable.
Outcome:	The proposed model decomposes questions into sub reasoning steps and is highly interpretable.

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Weiwen Xu, Ai Ti Aw, Yang Ding, Kui Wu, Shafiq Joty

Robustness in NLP

Challenge:	Recent advances in NMT have improved translation quality but are vulnerable to input perturbations.
Approach:	They propose a method to reduce the effect of noisy inputs by using a Context-Enhanced Reconstruction approach.
Outcome:	The proposed approach improves robustness on Chinese-English and French-English translation tasks.

DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction (2021.emnlp-main)

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Entony Lekhtman, Yftah Ziser, Roi Reichart

Sentiment Analysis

Challenge:	Existing methods for pre-training can be sub-optimal in some cases . for example, aspect extraction tasks require domain and category invariant representations .
Approach:	They propose a domain-invariant learning scheme for BERT to fine-tune pre-trained language models on a source domain and then apply it to a different target domain.
Outcome:	The proposed scheme improves performance over state-of-the-art models while using fraction of the unlabeled data.

Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages (2025.acl-srw)

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Daniil Gurgurov, Ivan Vykopal, Josef Van Genabith, Simon Ostermann

FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs (2024.naacl-long)

Challenge:	Low-resource languages (LRLs) face significant challenges in natural language processing due to limited data.
Approach:	They evaluate adapter-based methods for adapting mLMs to low-resource languages . they use unstructured text and structured knowledge from ConceptNet to evaluate adapters .
Outcome:	The proposed methods outperform large language models and LLaMA-3 and deepSeek-R1 models on low training data.

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Shamik Roy, Sailik Sengupta, Daniele Bonadiman, Saab Mansour, Arshit Gupta

Challenge:	Flow-adhering planning algorithm for task oriented dialogs (TODs) is a task-oriented dialog (TO) that can be used for task planning and API usage.
Approach:	They propose a Flow-Adhering Planning algorithm that follows predefined flows and preserves API dependencies in task oriented dialogs.
Outcome:	The proposed algorithm outperforms other decoding and prompting-based baselines in task oriented dialogs.

Towards Agile Text Classifiers for Everyone (2023.findings-emnlp)

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Maximilian Mozes, Jessica Hoffmann, Katrin Tomanek, Muhamed Kouate, Nithum Thain, Ann Yuan, Tolga Bolukbasi, Lucas Dixon

Information Retrieval

Challenge:	Text-based safety classifiers are widely used for content moderation and increasingly to tune generative language model behavior.
Approach:	They propose to use small, targeted datasets to train safety classifiers using small, iterative datasets that can be quickly developed for a particular policy.
Outcome:	The proposed method can be quickly developed for a specific policy with a labeled dataset of as few as 80 examples.

LoRA-MGPO: Mitigating Double Descent in Low-Rank Adaptation via Momentum-Guided Perturbation Optimization (2025.findings-emnlp)

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Yupeng Chang, Chenlu Guo, Yi Chang, Yuan Wu

AdaBERT-CTC: Leveraging BERT-CTC for Text-Only Domain Adaptation in ASR (2023.emnlp-industry)

Challenge:	Low-Rank Adaptation (LoRA) adapts large language models by training only a small fraction of parameters, but as the rank of the low-rank matrices increases, LoRA exhibits an unstable “double descent” phenomenon, which delays convergence and impairs generalization by causing instability due to the attraction to sharp local minima.
Approach:	They propose a framework that incorporates Momentum-Guided Perturbation Optimization (MGPO) MGPO stabilizes training dynamics by mitigating double descent phenomenon and guiding weight perturbations using momentum vectors from the optimizer’s state.
Outcome:	The proposed framework improves performance on natural language understanding benchmarks and shows that it improves convergence and generalization.

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Tyler Vuong, Karel Mundnich, Dhanush Bekal, Veera Elluru, Srikanth Ronanki, Sravan Bodapati

Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task (2022.acl-short)

Challenge:	End-to-end (E2E) automatic speech recognition models struggle to recognize out-of-domain words such as proper nouns and domain-specific terms.
Approach:	They propose a domain adaptation technique that relies solely on textual data to adapt to out-of-domain words.
Outcome:	The proposed method outperforms the base model by up to 14% relative word error rate improvement on several out-of-domain, publicly available datasets.

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Mohsen Tabasi, Kiamehr Rezaee, Mohammad Taher Pilehvar

State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models (2025.acl-short)

Challenge:	Existing few-shot approaches fail on the semantic distinction task of the Word-in-Context dataset.
Approach:	They propose a prompt-based approach which boosts few-shot performance to the level of fully supervised methods by using similarity metrics.
Outcome:	The proposed technique boosts few-shot performance to the level of fully supervised methods.

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Wonjun Kang, Kevin Galim, Yuchen Zeng, Minjae Lee, Hyung Il Koo, Nam Ik Cho

Task-driven Layerwise Additive Activation Intervention (2025.naacl-short)

Challenge:	State Space Models (SSMs) have emerged as efficient alternatives to Transformers, but their application to SSMs remains unexplored.
Approach:	They propose a state-based PEFT method that adjusts state directly instead of using external prompts.
Outcome:	The proposed method is based on state-offset tuning, which directly affects state at every timestep.

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Hieu Trung Nguyen, Bao Nguyen, Binh Nguyen, Viet Anh Nguyen

Source-Free Unsupervised Domain Adaptation for Question Answering via Prompt-Assisted Self-learning (2024.findings-naacl)

Challenge:	Existing approaches to task adaptation rely heavily on heuristic rules or prompt inputs.
Approach:	They propose a layer-wise additive activation intervention framework that steers the LMs’ generation process by identifying and manipulating the activations.
Outcome:	The proposed framework improves the accuracy of pretrained LMs and competing baselines on various datasets, demonstrating improvements in the accuracy and sample efficiency of the proposed framework.

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Maxwell Yin, Boyu Wang, Charles Ling

Low-Resource NLP

Challenge:	Existing SFDA methods focus on the adaptation phase, overlooking the impact of source domain training on model generalizability.
Approach:	They propose a source-free domain adaptation approach for Question Answering where a model trained on a domain is adapted to unlabeled target domains without additional source data.
Outcome:	The proposed model outperforms existing methods in managing domain gaps and demonstrating greater stability across target domains.

MTNT: A Testbed for Machine Translation of Noisy Text (D18-1)

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Paul Michel, Graham Neubig

Machine Translation

Challenge:	Noisy input text can cause disastrous mistranslations in most modern machine translation systems.
Approach:	They propose a benchmark dataset for Machine Translation of Noisy Text (MTNT) they use reddit comments and professionally sourced translations to examine noise types.
Outcome:	The proposed dataset can provide an attractive testbed for noise-robust machine translation systems.

Content Moderation for Evolving Policies using Binary Question Answering (2023.acl-industry)

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Sankha Subhra Mullick, Mohan Bhambhani, Suhit Sinha, Akshat Mathur, Somya Gupta, Jidnya Shah

“One-Size-Fits-All”? Examining Expectations around What Constitute “Fair” or “Good” NLG System Behaviors (2024.naacl-long)

Challenge:	Social media platforms use content moderation to safeguard users from abuse, harassment, malicious attacks, spam, etc.
Approach:	They propose to model content moderation as a binary question answering problem where questions validate loosely coupled themes constituting a policy.
Outcome:	The proposed model improves recall at 95% precision on two proprietary datasets of social media posts and comments respectively annotated under curated Hate Speech and Commercial Spam policies.

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Li Lucy, Su Lin Blodgett, Milad Shokouhi, Hanna Wallach, Alexandra Olteanu

Text Generation

Challenge:	Natural language generation models are used for many downstream applications involving interpersonal communication, such as text completion, "smart" reply suggestions, and chatbot assistants.
Approach:	They conduct five case studies that perturb identity-related language features in NLG inputs to examine their assumptions about fairness.
Outcome:	The findings highlight open challenges around what constitutes “fair” or “good” NLG system behaviors.

Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval (2023.findings-emnlp)

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Fan Jiang, Qiongkai Xu, Tom Drummond, Trevor Cohn

Thesis Proposal: Self-Adaptive and Epistemic Uncertainty-Guided ASR of Dense Intra-Sentential Code-Switched Speech for African Low-Resource Languages (2026.acl-srw)

Challenge:	Existing approaches to enhance dense retrieval models are unwieldy, such as requiring explicit supervision, complex model architectures, or massive external models.
Approach:	They propose an unsupervised method to enhance passage retrieval in zero-shot settings by iterating a loop that a dense retriever learns from supervision signals provided by a reranker.
Outcome:	The proposed method outperforms leading supervised and unsupervised retrievers on the BEIR benchmark while showing strong adaptation abilities to tasks and domains that were unseen during training.

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Umar Baba Umar, Sulaimon Adebayo Bashir, Abdulmalik Danlami Mohammed, Amina Gogo Tafida

Speech & Audio in NLP

Challenge:	Existing multilingual and pretrained ASR systems improve general recognition accuracy but are weak at switch regions and are sensitive to language imbalance during adaptation.
Approach:	They propose a self-adaptive and epistemic uncertainty-guided framework for African low-resource code-switched ASR using Hausa–English and Hausa-Yorùbá as case studies.
Outcome:	The proposed framework is based on Hausa–English and Hausa-Yorùbá as case studies.

ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion (2025.findings-emnlp)

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Rana Shahroz, Dongwen Tang, Pingzhi Li, Kai Wang, Tianlong Chen

As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages (2021.findings-acl)

Challenge:	Existing approaches to low-rank Adaptation (LoRA) are limited in scalability and controllability.
Approach:	They propose a conditional recurrent diffusion framework that generates LoRA parameters directly . they integrate model architecture and textual task specifications to generate task-specific parameters .
Outcome:	The proposed framework scales to billions-of-parameter LLMs and maintains controllability.

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Wietse de Vries, Malvina Nissim

Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization (2023.findings-emnlp)

Challenge:	Existing pre-trained language models are limited in their ability to train for English, which is a problem for many other languages.
Approach:	They propose to adapt existing generative language models to new languages by retraining lexical embeddings without tuning the Transformer layers.
Outcome:	The proposed method achieves lexical embeddings for Italian and Dutch that are aligned with the original English lexicals.

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Kaihang Pan, Juncheng Li, Hongye Song, Jun Lin, Xiaozhong Liu, Siliang Tang

Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension (2021.findings-emnlp)

Challenge:	Existing methods for prompt tuning can overfit to few-shot training samples, causing overfitting . authors propose a new framework for prompt learning with supervised meta-learning .
Approach:	They propose a self-supervised meta-prompt learning framework with MEta-gradient Regularization for few-shot generalization that leverages self-recognized meta-learning with a diverse set of meta-tasks to learn a universal prompt initialization using only unlabeled data.
Outcome:	The proposed framework learns a universal prompt initialization for efficient adaptation using only unlabeled data.

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Haichao Zhu, Zekun Wang, Heng Zhang, Ming Liu, Sendong Zhao, Bing Qin

Compact Personalized Models for Neural Machine Translation (D18-1)

Challenge:	Existing domain adaptation paradigms for reading comprehension require large amounts of annotation data to achieve the desired task performance.
Approach:	They propose a few-shot domain adaptation paradigm for reading comprehension . they introduce self-attention attribution to weigh parameters and refine the lottery subnetwork .
Outcome:	The proposed model outperforms the full model fine-tuning adaptation on four out of five domains with a small amount of data available for adaptation.

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Joern Wuebker, Patrick Simianer, John DeNero

Machine Translation

Challenge:	a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality.
Approach:	They propose gradient-based domain adaptation methods for self-attentive machine translation models . they encourage structured sparsity in the set of offset tensors during learning .
Outcome:	The proposed method achieves high space and time efficiency using sparse models . the results compare the proposed method with incremental adaptation .

EnerGIZAr: Leveraging GIZA++ for Effective Tokenizer Initialization (2025.findings-acl)

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Pranaydeep Singh, Eneko Agirre, Gorka Azkune, Orphee De Clercq, Els Lefever

Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers (2023.findings-eacl)

Challenge:	Continual pre-training has long been considered the default strategy for adapting models to non-English languages, but struggles with initializing new embeddings, especially for non-Latin scripts.
Approach:	They propose a method that leverages statistical word alignment techniques to improve continual pre-training by leveraging word alignment matrix between source and target tokens.
Outcome:	The proposed method outperforms existing methods on key NLP tasks including POS tagging, Sentiment Analysis, NLI, and NER in Hindi, Basque, Arabic and Korean.

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Chia-Chien Hung, Anne Lauscher, Dirk Hovy, Simone Paolo Ponzetto, Goran Glavaš

Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables (D19-1)

Challenge:	Existing studies show that incorporating demographic factors in language representations improves performance on downstream NLP tasks.
Approach:	They use continuous language modeling and dynamic multi-task learning to adapt pre-trained Transformers to incorporate demographic information into their representations.
Outcome:	The proposed model shows that the results are consistent with previous studies.

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Zihan Liu, Jamin Shin, Yan Xu, Genta Indra Winata, Peng Xu, Andrea Madotto, Pascale Fung

Low-Resource NLP

Challenge:	a lack of research on multilingual or cross-lingual task-oriented dialog systems has limited results . we propose a zero-shot adaptation of task-orientated dialog systems to low-resource languages . task-focused systems are often trained with monolingual datasets that are expensive to build or acquire .
Approach:	They propose a zero-shot adaptation of multilingual task-oriented dialog systems to low-resource languages using latent variables and a set of very few parallel word pairs.
Outcome:	The proposed model performs better in natural language understanding task compared to state-of-the-art model . the proposed model uses very few parallel word pairs to refine cross-lingual representations .

Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model (2025.findings-naacl)

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Jiali Chen, Xusen Hei, Yuqi Xue, Zihan Wu, Jiayuan Xie, Yi Cai

Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)

Challenge:	Recent large language models (LLMs) overlook children’s reading preferences, which poses challenges in CLA.
Approach:	They propose a method that augments large language models with children's reading preferences for adaptation by obtaining characters' personalities and narrative structure as additional information for fine-grained instruction tuning.
Outcome:	The proposed method significantly improves performance in automatic and human evaluation.

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Bowen Dong, Yuan Yao, Ruobing Xie, Tianyu Gao, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

One Tokenizer To Rule Them All: Emergent Language Plasticity via Multilingual Tokenizers (2026.acl-long)

Challenge:	Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability.
Approach:	They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework .
Outcome:	The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation.

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Diana Abagyan, Alejandro R. Salamanca, Andres Felipe Cruz-Salinas, Kris Cao, Hangyu Lin, Acyr Locatelli, Marzieh Fadaee, Ahmet Üstün, Sara Hooker

Syntactic Text Processing

Challenge:	Existing approaches to train multilingual large language models for many languages at once are limited due to limited model capacity, scarce high-quality data, and compute constraints.
Approach:	They propose to use a universal tokenizer to improve language plasticity and adaptability to new languages by up to 20%.
Outcome:	The proposed tokenizer improves language plasticity and improves plasticity towards languages that are completely unseen in the tokenizer and pretraining, by up to 5% win rate gain.

Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives (2020.lrec-1)

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Yudai Kishimoto, Yugo Murawaki, Sadao Kurohashi

Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (2023.findings-emnlp)

Challenge:	Existing studies on the performance of BERT for implicit discourse relation classification have not been conducted.
Approach:	They propose to apply BERT to implicit discourse relation classification by performing additional pre-training on text tailored to discourse relations.
Outcome:	The proposed methods outperform previous state-of-the-art models in many tasks.

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Xilai Ma, Jing Li, Min Zhang

Low-Resource NLP

Challenge:	Existing approaches to few-shot relation extraction require training.
Approach:	They propose a method for few-shot relation extraction using large language models, called CoT-ER, chain-of-thought with explicit evidence reasoning.
Outcome:	The proposed approach achieves competitive performance compared to the fully-supervised state-of-the-art approach on the FewRel1.0 and FewRela2.0 datasets.

Simple, Scalable Adaptation for Neural Machine Translation (D19-1)

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Ankur Bapna, Orhan Firat

Topic Sensitive Attention on Generic Corpora Corrects Sense Bias in Pretrained Embeddings (P19-1)

Challenge:	Recent advances in deep learning have led to significantly improved quality on Neural Machine Translation (NMT) however, performance on out-of-domain data or low resource languages remains poor.
Approach:	They propose a simple yet efficient approach for adapting pre-trained models to multiple tasks simultaneously.
Outcome:	The proposed approach is on par with full fine-tuning on domain adaptation and massively multilingual NMT on a massively multilingual dataset.

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Vihari Piratla, Sunita Sarawagi, Soumen Chakrabarti

Large Language Models Have Intrinsic Meta-Cognition, but Need a Good Lens (2025.emnlp-main)

Challenge:	Existing methods to adapt pretrained embeddings to a large corpus are limited and do not provide sufficient quality.
Approach:	They propose to use a small corpus D_T to pretrain embeddings that accurately capture the sense of words in a limited set of focused topics.
Outcome:	The proposed embeddings capture the sense of words in a topic in spite of the limited size of the corpus D_T.

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Ziyang Ma, Qingyue Yuan, Zhenglin Wang, Deyu Zhou

LoRAN: Improved Low-Rank Adaptation by a Non-Linear Transformation (2024.findings-emnlp)

Challenge:	Existing studies have focused on the cognitive error detection capabilities of Large Language Models (LLMs), but few studies have examined the meta-cognitive abilities of LLMs.
Approach:	They propose an automated meta-cognition evaluation framework for evaluation of LLMs and a Markovian Intrinsic Reward Adjustment strategy to boost current lenses.
Outcome:	The proposed framework can be used to evaluate the meta-cognition abilities of LLMs and improve them.

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Yinqiao Li, Linqi Song, Hanxu Hou

BiKT: Enabling Bidirectional Knowledge Transfer Between Pretrained Models and Sequential Downstream Tasks (2024.findings-emnlp)

Challenge:	Recent methods for fine-tuning large language models have shown great improvements on a wide range of NLP tasks.
Approach:	They propose to introduce a non-linear transformation to improve performance of adapters by introducing a low-rank adaptation to fit the accumulated weight updates.
Outcome:	The proposed method outperforms a baseline on SAMSum and 20 Newsgroups tasks and even improves the classification task by 1.95 points when a lower rank is applied.

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Hang Zeng, Chaoyue Niu, Fan Wu, Shaojie Tang, Leihao Pei, Chengfei Lv, Guihai Chen

Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval (2024.acl-long)

Challenge:	Existing frameworks adapt from initial pretrained model to each downstream task directly, but ignore sequential nature of downstream tasks and feedback effect on pretrained models.
Approach:	They propose a framework to enable bidirectional knowledge transfer between pretrained models and downstream tasks in rounds.
Outcome:	The proposed framework improves on 9 GLUE datasets and 6 SuperGLUEs.

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Chaofan Li, Zheng Liu, Shitao Xiao, Yingxia Shao, Defu Lian

DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs (D19-1)

Challenge:	Dense retrieval requires discriminative embeddings to represent the semantic relationship between query and document.
Approach:	They propose an unsupervised approach that performs unsupervised adaptation of large language models for dense retrieval.
Outcome:	The proposed model improves on a variety of dense retrieval benchmarks and is available on github.

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Yi-Lin Tuan, Yun-Nung Chen, Hung-yi Lee

Dialogue Systems & Conversational Agents

Challenge:	Existing work has not shown that knowledge-grounded models can zero-shot adapt to updated, unseen knowledge graphs.
Approach:	They propose a task to apply dynamic knowledge graphs to neural conversation models . they propose 'dyKgChat' that selects an output from two networks at each time step .
Outcome:	The proposed model outperforms existing knowledge-grounded conversation models in evaluation metrics.

Convolutional LoRA Aggregation for Unseen Tasks Adaptation (2025.findings-emnlp)

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Xinhao Wu, Jialin Liu, Yutai Duan, Jie Liu

Challenge:	Existing LoRA selection methods rely on a few task samples, making it difficult to capture full scope of task-relevant information.
Approach:	They propose a framework that selects appropriate LoRA modules and aggregates them using a convolutional LoRA aggregator.
Outcome:	The proposed framework bridges the knowledge gap between selected modules and target task . it ensures comprehensive coverage of task-relevant LoRA modules .

Measuring Immediate Adaptation Performance for Neural Machine Translation (N19-1)

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Patrick Simianer, Joern Wuebker, John DeNero

Machine Translation

Challenge:	Incremental domain adaptation improves interactive machine translation performance . users of interactive systems are sensitive to the speed of adaptation .
Approach:	They propose to measure the speed of lexical acquisition for in-domain vocabulary . they propose to use this to choose the most suitable adaptation method for neural machine translation .
Outcome:	The proposed measures measure the speed of lexical acquisition for in-domain vocabulary . they show that the most suitable adaptation method is chosen from a range of different techniques .

Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media (2021.findings-emnlp)

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Paul Röttger, Janet Pierrehumbert

Information Extraction & Text Mining

Challenge:	Language use differs between domains and even within a domain, language use changes over time.
Approach:	They propose to use social media comments to study temporal adaptations in pre-trained language models.
Outcome:	The proposed model performs better on past than on future test sets, whereas adapting to domain does not improve performance on the downstream task.

Domain Confused Contrastive Learning for Unsupervised Domain Adaptation (2022.naacl-main)

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Quanyu Long, Tianze Luo, Wenya Wang, Sinno Pan

Sparse Low-rank Adaptation of Pre-trained Language Models (2023.emnlp-main)

Challenge:	Existing studies on domain-shifting adaptations have focused on domain .
Approach:	They propose a self-supervised approach to unsupervised domain adduction using domain puzzles to bridge the source and target domains and retain discriminative representations after adaptation.
Outcome:	The proposed approach outperforms baselines and further ablation studies show that it is more stable and effective when performing other data augmentations.

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Ning Ding, Xingtai Lv, Qiaosen Wang, Yulin Chen, Bowen Zhou, Zhiyuan Liu, Maosong Sun

X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering (2021.naacl-main)

Challenge:	Existing methods for fine-tuning pre-trained large language models in a parameter-efficient manner are gaining traction within the research community.
Approach:	They propose a method of low-rank adaptation that enables dynamic adjustments to the intrinsic rank during the adaptation process.
Outcome:	The proposed approach outperforms the current method with a fixed and unalterable intrinsic rank and a low-rank adaptation process.

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Meryem M’hamdi, Doo Soon Kim, Franck Dernoncourt, Trung Bui, Xiang Ren, Jonathan May

Multilinguality

Challenge:	Multilingual models have gained popularity for their zero-shot cross-lingual transfer learning capabilities, but their generalization ability is inconsistent for typologically diverse languages.
Approach:	They propose a meta-learning approach that adapts MAML to learn to adapt to new languages . they extensively evaluate two cross-lingual NLU tasks using English as source and spanish as target .
Outcome:	The proposed approach outperforms naive fine-tuning on cross-lingual tasks for most languages.

Class-Incremental Few-Shot Event Detection (2024.lrec-main)

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Kailin Zhao, Xiaolong Jin, Long Bai, Jiafeng Guo, Xueqi Cheng

Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language (C18-1)

Challenge:	Existing methods to deal with new class of events with only a few labeled instances are challenging . old knowledge forgetting and new class overfitting are two problems in this task.
Approach:	They propose a task called class-incremental few-shot event detection to solve old knowledge forgetting and new class overfitting problems.
Outcome:	The proposed method reduces old knowledge forgetting and new class overfitting problems on two benchmark datasets.

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He Bai, Yu Zhou, Jiajun Zhang, Liang Zhao, Mei-Yuh Hwang, Chengqing Zong

Information Extraction & Text Mining

Challenge:	a study aims to develop a language transferring system to avoid the trouble of acquiring and labeling a new big SLU corpus . general-purpose translators cannot handle the lot of semantic labels, not to mention cultural differences . a RL-based language transfer method can be used to adapt the adapted translator to a target language .
Approach:	They propose to use reinforcement learning to adapt a spoken language understanding model to a target language.
Outcome:	The proposed language transferring method improves domain classification accuracy by 22% compared with naive translation . the proposed language transfer method can be used on Chinese to English translators with more proper slot tags .

Spectral Disentanglement: Rank-Aware Task Adaptation for Rehearsal-free Continual Learning in LLMs (2026.acl-long)

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Huanxuan Liao, Shizhu He, Yupu Hao, Yequan Wang, Wenhao Teng, Xiangwen Liao, Jun Zhao, Kang Liu

Green & Sustainable NLP

Challenge:	Continual Learning (CL) for Large Language Models faces a fundamental Stability-Plasticity Dilemma . Rank-Blindness enforces a single rank constraint across diverse tasks, leading to catastrophic forgetting of earlier tasks and underfitting on complex new ones.
Approach:	They propose a rank-spectrum-based rehearsal-free framework that explicitly disentangles knowledge into two orthogonal subspaces.
Outcome:	The proposed framework achieves a superior stability-plasticity balance compared to single-rank baselines.

ProDial – An Annotated Proactive Dialogue Act Corpus for Conversational Assistants using Crowdsourcing (2022.lrec-1)

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Matthias Kraus, Nicolas Wagner, Wolfgang Minker

Natural Language Interfaces

Challenge:	Especially in the household domain, robots may become indispensable helpers by overtaking tedious tasks, e.g. keeping the place tidy.
Approach:	They propose a conversational approach for explicitly collecting personal user information using natural dialogue.
Outcome:	The proposed approach is compared to a baseline dialogue strategy for interactive personalization and has shown that it is friendlier.

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs (2023.findings-emnlp)

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Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Arik, Tomas Pfister, Somesh Jha

Challenge:	Large language models (LLMs) have shown impressive capabilities in many tasks, including natural language understanding and generation.
Approach:	They propose a framework for adaptation with self-evaluation to improve selective prediction performance of large language models.
Outcome:	The proposed framework outperforms state-of-the-art selective prediction methods on QA datasets and improves the AUACC from 91.23% to 92.63% and AUROC from 74.61% to 80.25%.

The Hidden Space of Transformer Language Adapters (2024.acl-long)

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Jesujoba Alabi, Marius Mosbach, Matan Eyal, Dietrich Klakow, Mor Geva

Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality (2026.eacl-long)

Challenge:	Adapters are small modules trained on top of a frozen language model to adapt predictions to new target languages.
Approach:	They propose to train transformer language adapters on top of a frozen model to adapt predictions to new target languages.
Outcome:	The transformer language adapters are trained on top of a frozen model to adapt predictions to new target languages.

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Jana Jung, Marlene Lutz, Indira Sen, Markus Strohmaier

It’s What You Say and How You Say It: Investigating the Effect of Linguistic vs. Behavioral Adaptation in Task-Oriented Chatbots (2025.coling-main)

Challenge:	Psychometric tests are increasingly used to assess psychological constructs in large language models (LLMs).
Approach:	They evaluate the reliability and validity of human psychometric tests on 17 LLMs for three constructs: sexism, racism, and morality.
Outcome:	The results show that the psychometric tests on 17 LLMs do not align, and in some cases negatively correlate with, model behavior in downstream tasks, indicating low ecological validity.

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Lindsey Vanderlyn, Ngoc Thang Vu

Natural Language Interfaces

Challenge:	linguistic adaptation is not known to have a positive impact on dialog success and user perception.
Approach:	They evaluate subjective and objective aspects of dialog success and user perceptions through a user study . they also examine linguistic adaptations of dialog agents to determine which aspects influence user perception .
Outcome:	The proposed agents can differ in their level of formality and their linguistic style.

DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning (2024.lrec-main)

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Mengfei Du, Binhao Wu, Jiwen Zhang, Zhihao Fan, Zejun Li, Ruipu Luo, Xuanjing Huang, Zhongyu Wei

Lifelong Sequence Generation with Dynamic Module Expansion and Adaptation (2023.emnlp-main)

Challenge:	Existing studies focus on cross-modal attention at the fusion stage, but modality features generated by disparate uni-encoders reside in their own spaces, leading to a decline in the quality of cross-modulation and decision-making.
Approach:	They propose a framework to align navigation-related modalities before fusion by cross-modal contrastive learning.
Outcome:	The proposed framework integrates with the majority of existing models, resulting in improved navigation performance on various VLN benchmarks, including R2R, R4R, and CVDN.

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Chengwei Qin, Chen Chen, Shafiq Joty

Text Generation

Challenge:	Existing methods focus on maintaining old knowledge while paying little attention to knowledge transfer across tasks.
Approach:	They propose to train a model on a sequence of generation tasks to learn new generation patterns while avoiding the forgetting of previous knowledge.
Outcome:	The proposed model outperforms existing methods in different settings.

Continual Dialogue State Tracking via Reason-of-Select Distillation (2024.findings-acl)

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Yujie Feng, Bo Liu, Xiaoyu Dong, Zexin Lu, Li-Ming Zhan, Xiao-Ming Wu, Albert Lam

Dialogue Systems & Conversational Agents

Challenge:	Existing research on dialogue systems has focused on domain-specific offline systems lacking adaptation abilities.
Approach:	They propose a Reason-of-Select distillation method that enhances smaller models with a novel "meta-reasoning" capability.
Outcome:	Experiments show that the proposed method significantly improves the performance and generalization capabilities of existing models.

Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification (2022.coling-1)

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Suhe Wang, Xiaoyuan Liu, Bo Liu, Diwen Dong

Adaptive Cross-lingual Text Classification through In-Context One-Shot Demonstrations (2024.naacl-long)

Challenge:	Existing studies fail to consider the importance of the semantic interaction between sentence features and neglect to enhance the generalization ability of the model to new tasks.
Approach:	They propose to integrate an adversarial network architecture into the meta-learning system and leverage cost-effective modules to build a few-shot classification framework called SaAML.
Outcome:	The proposed framework outperforms state-of-the-art methods on four benchmark datasets.

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Emilio Cueva, Adrian Lopez Monroy, Fernando Sánchez-Vega, Thamar Solorio

Cross-Lingual Transfer

Challenge:	Zero-Shot Cross-lingual transfer (ZS-XLT) uses a model trained in a source language to make predictions in another language, often with a performance loss.
Approach:	They propose a new approach that uses In-Context Tuning to train a model to learn from context examples and adapt it to a target language by prepending a One-Shot context demonstration.
Outcome:	The proposed approach outperforms prompt-based models in Zero-Shot and Few-shot scenarios with target-language examples.

LaRA: Large Rank Adaptation for Speech and Text Cross-Modal Learning in Large Language Models (2024.findings-emnlp)

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Zuhair Shaik, Pradyoth Hegde, Prashant Bannulmath, Deepak T

Soft Alignment Objectives for Robust Adaptation of Language Generation (2023.acl-long)

Challenge:	Existing approaches to integrate speech and text capabilities into large language models (LLMs) require significantly larger ranks comparable to the pretrained weights to accommodate the complexities of speech-text cross-modality learning.
Approach:	They propose a large-rank adaptive approach for cross-modal integration of speech and text into large language models (LLMs) it uses a Hi-Fi vocoder to synthesize speech waveforms from the generated speech units.
Outcome:	The proposed model can be extended to other cross-modal applications.

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Michal Štefánik, Marek Kadlcik, Petr Sojka

How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input? (2022.coling-1)

Challenge:	Domain adaptation is a common approach for generative language models, but it is notorious for over-specialization to the target domain, resulting in catastrophic forgetting.
Approach:	They propose to build training objectives on a semantic similarity of predicted tokens to the reference and avoid catastrophic forgetting of adaptation by preserving adaptation in-domain quality.
Outcome:	The proposed objectives mitigate catastrophic forgetting while preserving the adaptation in-domain quality while reducing computational costs.

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Lovisa Hagström, Richard Johansson

DenseLoRA: Dense Low-Rank Adaptation of Large Language Models (2025.acl-long)

Challenge:	Current language models have been criticised for learning language from text alone without connection between words and their meaning.
Approach:	They propose to train models on more sources than text to provide the lacking connection between words and their meanings.
Outcome:	The proposed model adaptation methods perform differently for different models and unimodal model counterparts perform on par with the VL models regardless of adaptation.

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Lin Mu, Xiaoyu Wang, Li Ni, Yang Li, Zhize Wu, Peiquan Jin, Yiwen Zhang

Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation (2022.lrec-1)

Challenge:	Low-rank adaptation (LoRA) is an efficient approach for adapting large language models (LLMs) but many of the weights in these matrices are redundant, leading to inefficiencies in parameter utilization.
Approach:	They propose a low-rank adaptation approach that fine-tunes two low-ranked matrices and adapts them through a dense low-Rank matrix, improving parameter utilization and adaptation efficiency.
Outcome:	The proposed approach achieves 83.8% accuracy with only 0.01% of trainable parameters compared to LoRA's 80.8% with 0.70% of trainability parameters on LLaMA3-8B.

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Elise Bertin-Lemée, Annelies Braffort, Camille Challant, Claire Danet, Boris Dauriac, Michael Filhol, Emmanuella Martinod, Jérémie Segouat

Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering (2023.acl-long)

Challenge:	a new corpus of french Sign Language (LSF) data is created to support future studies on the automatic translation of written French into LSF, rendered through the animation of a virtual signer.
Approach:	They propose to use a French Sign Language corpus called "Rosetta-LSF" it is intended to support studies on automatic translation of written French into LSF .
Outcome:	The proposed corpus supports future studies on automatic translation of written French into LSF, rendered through animation of a virtual signer.

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Hai Ye, Qizhe Xie, Hwee Tou Ng

Natural Language Interfaces

Challenge:	Recent research on test-time adaptation suggests a possible way to improve the generalization ability of LLMs.
Approach:	They propose to use multi-armed bandit learning and multi-arm dueling bandits to solve a multi-source test-time model adaptation problem from user feedback.
Outcome:	The proposed model is more effective than other strong baselines on extractive question answering datasets.

Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer (2022.emnlp-main)

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Ahmet Üstün, Arianna Bisazza, Gosse Bouma, Gertjan van Noord, Sebastian Ruder

Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech (2021.emnlp-main)

Challenge:	Existing multilingual models cannot fully leverage training data when it is available in different task-language combinations.
Approach:	They propose a single hypernetwork that unifies multi-task and multilingual learning with efficient adaptation.
Outcome:	The proposed model achieves the best or competitive gain when a mixture of multiple resources is available while being significantly more efficient than existing models.

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Katrin Tomanek, Vicky Zayats, Dirk Padfield, Kara Vaillancourt, Fadi Biadsy

Speech & Audio in NLP

Challenge:	Automatic Speech Recognition systems perform poorly on atypical speech and heavily accented speech.
Approach:	They add a residual adapter to the encoder layer to improve model adaptation . they show that the residual adapters update only a tiny fraction of the model parameters .
Outcome:	The proposed model fine-tuning improves performance on atypical and accented speech . the system can update only a tiny fraction of the model parameters .

RAG or Learning? Understanding the Limits of LLM Adaptation under Continuous Knowledge Drift in the Real World (2026.findings-acl)

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Hanbing Liu, Lang Cao, Yang Li

Information Retrieval

Challenge:	Existing methods to update or supplement large language models struggle under continuous knowledge drift.
Approach:	They propose a dynamic event benchmark and time-aware retrieval baseline that captures how knowledge evolves over time.
Outcome:	The proposed method enables systematic evaluation of model adaptation under continuous knowledge drift.

Grounded Adaptation for Zero-shot Executable Semantic Parsing (2020.emnlp-main)

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Victor Zhong, Mike Lewis, Sida I. Wang, Luke Zettlemoyer

Large Language Models Are Partially Primed in Pronoun Interpretation (2023.findings-acl)

Challenge:	Existing semantic parsers are usually engineered for each application environment, but they struggle when deployed to a new database.
Approach:	They propose a method to adapt existing semantic parsers to new environments . they propose combining a forward semantic parsed with a backward utterance generator to synthesize data in the new environment and select cycle-consistent examples to adapt the parser.
Outcome:	The proposed procedure outperforms data-augmentation and improves execution accuracy on the Spider, Sparc, and CoSQL zero-shot semantic parsing tasks.

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Suet-Ying Lam, Qingcheng Zeng, Kexun Zhang, Chenyu You, Rob Voigt

SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control (2023.acl-long)

Challenge:	Existing studies suggest large language models acquire rich linguistic representations, but little is known about whether they adapt to linguistic biases in a human-like way.
Approach:	They examine whether large language models display human-like referential biases using stimuli and procedures from real psycholinguistic experiments.
Outcome:	The proposed models display human-like referential biases when exposed to referential patterns in the local context.

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Xiaochuang Han, Sachin Kumar, Yulia Tsvetkov

Coarse-to-Fine Grounded Memory for LLM Agent Planning (2025.emnlp-main)

Challenge:	Existing diffusion models for continuous-valued domains have not been adopted for text data.
Approach:	They propose a diffusion-based language model with two key design choices . semi-autoregressive model generates blocks of text and allows local context updates . they evaluate it on unconstrained text generation benchmarks .
Outcome:	The proposed model outperforms autoregressive models on unconstrained text generation benchmarks on uncontrolled text generation.

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Wei Yang, Jinwei Xiao, Hongming Zhang, Qingyang Zhang, Yanna Wang, Bo Xu

Challenge:	Existing methods to enhance LLM with offline experiences or online trajectory analysis focus on single-granularity memory derived from dynamic environmental interactions.
Approach:	They propose a framework that grounds coarse-to-fine memories with LLM to enable flexible adaptation to diverse scenarios.
Outcome:	Extensive experiments on AlfWorld, Webshop and ScienceWorld show that the proposed framework outperforms baselines and comprehensively optimizes memory-enhanced LLM Agent system.

Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem (2020.acl-main)

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Danielle Saunders, Bill Byrne

Retrieval Enhanced Feedback via In-context Neural Error-book (2025.emnlp-main)

Challenge:	Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to women than to men.
Approach:	They propose a lattice-rescoring scheme which allows a trade-off between general translation quality and bias reduction during adaptation and inference time.
Outcome:	The proposed approach outperforms all systems evaluated on WinoMT with no degradation of general test set BLEU.

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Jongyeop Hyun, Bumsoo Kim

NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment Analysis (2021.emnlp-main)

Challenge:	Existing methods for learning from errors lack a structured framework for analyzing and mitigating errors, especially in Multimodal Large Language Models (MLLMs).
Approach:	They propose a teacher-student framework that systematically structures errors to deliver targeted feedback for multimodal reasoning.
Outcome:	The proposed framework improves inference efficiency, token usage, and scalability by building a query-based structure that prioritizes visual information, diagnoses failure points, and guides corrective actions.

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Nikolay Arefyev, Dmitrii Kharchev, Artem Shelmanov

A Unified Speaker Adaptation Approach for ASR (2021.emnlp-main)

Challenge:	Pre-training Masked Language Models (MLMs) on massive datasets is expensive, but it is performed for each domain or task individually and is resource-demanding.
Approach:	They propose a method for more efficient adaptation that focuses on predicting words with large weights of the Naive Bayes classifier trained for the task at hand.
Outcome:	The proposed method improves sentiment analysis by focusing on predicting words with large weights of the Naive Bayes classifier trained for the task at hand.

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Yingzhu Zhao, Chongjia Ni, Cheung-Chi Leung, Shafiq Joty, Eng Siong Chng, Bin Ma

Exploring LLM Annotation for Adaptation of Clinical Information Extraction Models under Data-sharing Restrictions (2025.findings-acl)

Challenge:	Adapting a model to target speakers requires a lot of compute and may cause catastrophic forgetting to the existing speakers.
Approach:	They propose a unified speaker adaptation approach consisting of feature adaptation and model adaptation.
Outcome:	The proposed model outperforms baseline models with 20.58% relative WER reduction and surpasses finetuning method by 2.54% on target speaker adaptation.

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Seiji Shimizu, Hisada Shohei, Yutaka Uno, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki

Entailment as Robust Self-Learner (2023.acl-long)

Challenge:	In-hospital text data often contains valuable clinical information, yet fine-tuned small language models (SLMs) for information extraction remain challenging due to differences in formatting and vocabulary across institutions.
Approach:	They leverage large language models to annotate the target domain data for adaptation . they use in-hospital text data to extract clinical information .
Outcome:	The proposed model outperforms manual annotation on four clinical information extraction tasks with a larger number of annotated data.

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Jiaxin Ge, Hongyin Luo, Yoon Kim, James Glass

Do Slides Help? Multi-modal Context for Automatic Transcription of Conference Talks (2025.emnlp-main)

Challenge:	Recent studies have found that entailment pretraining benefits weakly supervised fine-tuning.
Approach:	They propose a prompting strategy that formulates different NLU tasks as contextual entailment and propose an algorithm for better pseudo-labeling quality in self-training.
Outcome:	The proposed approach improves the zero-shot adaptation performance on downstream tasks.

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Supriti Sinhamahapatra, Jan Niehues

Adapting Where It Matters: Depth-Aware Adaptation for Efficient Multilingual Speech Recognition in Low-Resource Languages (2026.findings-acl)

Challenge:	Current automatic speech recognition systems rely on only audio information, ignoring multi-modal context.
Approach:	They propose to integrate visual context into existing automatic speech recognition systems to integrate presentation slides with multi-modal information.
Outcome:	The proposed model reduces word error rate by approximately 34% across all words and 35% for domain-specific terms compared to baseline model.

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Yang Xiao, Eun-Jung Holden, Ting Dang

TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models (2026.acl-long)

Challenge:	Recent speech foundation models excel at multilingual automatic speech recognition (ASR) for high-resource languages, but their performance drops substantially on low-resourced languages due to the limited data availability.
Approach:	They propose a Depth-Aware Model Adaptation framework that allocates adaptation capacity according to each layer’s role.
Outcome:	The proposed framework matches or surpasses state-of-the-art accuracy with 80% fewer trainable parameters and achieves 29% error reduction under extreme data scarcity.

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Lin Mu, Haiyang Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

FLEXITOKENS: Flexible Tokenization for Evolving Language Models (2026.findings-acl)

Challenge:	Existing LoRA methods assume that experts operate independently, leading to unstable routing, expert dominance.
Approach:	They propose a communication-aware MoELoRA framework that relaxes this assumption by introducing expert-level communication prior to routing.
Outcome:	The proposed framework outperforms vanilla LoRA and MoELoRA on diverse language understanding tasks while maintaining expert dominance.

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Abraham Toluwase Owodunni, Orevaoghene Ahia, Sachin Kumar

Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles (2024.findings-emnlp)

Challenge:	Widely used subword tokenizers overfragment sequences in unseen domains, languages, and scripts . inefficient tokenizer models can cause overfragments in out-of-distribution domains if not trained properly .
Approach:	They propose a byte-level LM with learnable tokenizers to make tokenization adaptive . they propose 'flexitoken' which enables significantly greater flexibility during adaptation .
Outcome:	The proposed method significantly reduces token overfragmentation and improves on multilingual benchmarks and domains.

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Xiao Pu, Tianxing He, Xiaojun Wan

Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates (2026.acl-long)

Challenge:	Prompt compression reduces inference time and costs while maintaining informativeness for different usage scenarios.
Approach:	They propose a framework that adapts a smaller language model to compress prompts for a larger model on a new task without additional training.
Outcome:	The proposed framework outperforms two baseline models in four tasks . iteratively generates and selects effective compressed prompts as task-specific demonstrations .

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Atsuki Yamaguchi, Terufumi Morishita, Aline Villavicencio, Nikolaos Aletras

CoCo: A Tool for Automatically Assessing Conceptual Complexity of Texts (2020.lrec-1)

Challenge:	Large language models underperform in languages absent or underrepresented in training data, creating barrier to equitable access for speakers worldwide.
Approach:	They propose a selective parameter update strategy that proactively preserves source knowledge by identifying critical parameters critical to maintaining source abilities.
Outcome:	Experiments in five typologically diverse languages show that SSU mitigates catastrophic forgetting.

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Sanja Stajner, Sergiu Nisioi, Ioana Hulpuș

Text Complexity

Challenge:	Traditional text complexity assessment only takes into account lexical and lexiconal complexity.
Approach:	They propose a tool for automatic assessment of conceptual text complexity based on the current state-of-the-art unsupervised approach . they compare the current implementation with the state of the art and discuss the influence of the choice of entity linker on the performance of the tool.
Outcome:	The proposed tool can be personalized and adapted to the needs of struggling readers.

Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models (2025.acl-long)

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Yuheng Lu, Bingshuo Qian, Caixia Yuan, Huixing Jiang, Xiaojie Wang

MAFMO: Multi-modal Adaptive Fusion with Meta-template Optimization for Vision-Language Models (2025.findings-emnlp)

Challenge:	Existing approaches to mitigate catastrophic forgetting can be broadly categorized into data-based, architecture-based and learning-based methods.
Approach:	They propose a subspace regularization method on LoRA structure that imposes constraints on direction of updating matrix’s null space.
Outcome:	The proposed method reduces scale of output change while introducing minimal constraint on model capacity.

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Mingrui Xie, Lulu Xu, Junliang Du

Analyzing Film Adaptation through Narrative Alignment (2023.emnlp-main)

Challenge:	Existing approaches focus on single-modality adjustments, leading to suboptimal alignment and limited generalization.
Approach:	They propose a plug-and-play framework for visual recognition that integrates a Harmonic Cross-Modal Adapter and a Meta-Template Optimization module.
Outcome:	Extensive experiments across multiple fine-grained visual recognition benchmarks show that MAFMO consistently improves existing methods’ performance on both novel classes and harmonic mean while maintaining robustness under various challenging conditions with minimal computational overhead.

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Tanzir Pial, Shahreen Aunti, Charuta Pethe, Allen Kim, Steven Skiena

Challenge:	a new study examines the book-to-film adaptation process by examining the differences between the two media . novel adaptations often require dropping sections of the source text from the movie script .
Approach:	They use a Smith-Waterman local alignment algorithm to quantify text similarity between scenes and book units.
Outcome:	The proposed method reveals that novel adaptations often require dropping parts of the source text from the movie script.

NormAL LoRA: What is the perfect size? (2025.findings-emnlp)

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null Aastik, Topu Sai Meghana, Chinmay Prakash Kulkarni, Pragya Paramita Sahu

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) are crucial for enabling intelligent experiences across applications.
Approach:	They propose a low-rank adaptive localization method that uses rank-norm regularization to determine the optimal rank for each weight matrix.
Outcome:	NormAL LoRA reduces adapter parameters by 37% while preserving full fine-tuning performance.

Can Spectral-Clipping Enable Better Learning While Forgetting Less for Low-Rank Adaptation? (2026.acl-long)

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Hyowon Wi, Noseong Park

CogSteer: Cognition-Inspired Selective Layer Intervention for Efficiently Steering Large Language Models (2025.findings-acl)

Challenge:	In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning the full set of parameters.
Approach:	They propose a low-rank adaptation approach that injects parameterized singular components with spectral clipping into the pre-trained model.
Outcome:	The proposed method improves performance and retains pre-trained knowledge while preserving the weights of the model.

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Xintong Wang, Jingheng Pan, Liang Ding, Longyue Wang, Longqin Jiang, Xingshan Li, Chris Biemann

Unveiling the Potential of BERT-family: A New Recipe for Building Scalable, General and Competitive Large Language Models (2025.acl-long)

Challenge:	Large Language Models (LLMs) achieve excellent performance through pretraining on extensive data.
Approach:	They propose an efficient selective layer intervention based on parameter-efficient fine-tuning methods to select the optimal steering layer to modulate LLM semantics.
Outcome:	The proposed approach is based on a model-agnostic framework and is safe to deploy.

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Yisheng Xiao, Juntao Li, Wenpeng Hu, Zhunchen Luo, Min Zhang

Probing and Boosting Large Language Models Capabilities via Attention Heads (2025.emnlp-main)

Challenge:	Generative large language models (LLMs) have significantly influenced various aspects of society, reshaping how we access and interact with information and knowledge.
Approach:	They propose a pre-training task that helps BERT-family excel in wider applications . they also explore the integration of cutting-edge technologies into their models to further enhance their capabilities.
Outcome:	The proposed model exhibits performance levels comparable to current SOTA LLMs across a spectrum of tasks.

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Dezhi Zhao, Xin Liu, Xiaocheng Feng, Hui Wang, Bing Qin

LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation (2026.findings-acl)

Challenge:	Existing approaches to identifying capabilities rely on external signals with limited structural grounding . emergence of specific capabilities remains poorly understood .
Approach:	They propose a lightweight approach that links LLM capabilities to internal components by identifying correspondences at the level of attention heads.
Outcome:	The proposed approach improves accuracy on MMLU and BBH by 1 to 1.5 points over gradient-based method and 5 to 6 points over other intermediate-state baselines.

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Jin Zhong, Jinglin Liang, Tongtong Yang, Zijian Xie, Shuangping Huang, Hanlin Gu