Papers with transferability
Transferability of Syntax-Aware Graph Neural Networks in Zero-Shot Cross-Lingual Semantic Role Labeling (2024.findings-emnlp)
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| Challenge: | Existing studies in cross-lingual semantic role labeling (SRL) lack a comprehensive analysis of their network selection. |
| Approach: | They compare the transferability of graph neural network-based models with universal dependency trees to English and 23 target languages. |
| Outcome: | The proposed models perform better in resource-poor languages than in resource rich ones. |
Query-Efficient Textual Adversarial Example Generation for Black-Box Attacks (2024.naacl-long)
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| Challenge: | Existing black-box attacks require thousands of queries on the target model, making them expensive in real-world applications. |
| Approach: | They propose a new approach that guides word substitutions using prior knowledge from the training set to improve the attack efficiency. |
| Outcome: | The proposed approach reduces query-free attack and guided search attacks by a factor of 10 500 . it improves transferability and generalization by the ensemble of the ABPens in NLP . |
Dagger Behind Smile: Fool LLMs with a Happy Ending Story (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have attracted significant attention from jailbreak attacks . existing manual designs are either easily detectable or require intricate interactions with LLMs. |
| Approach: | They propose a happy ending attack that wraps up a malicious request in a scenario template . |
| Outcome: | The proposed attack wraps up a malicious request in a scenario template involving a positive prompt formed mainly via a happy ending, fooling LLMs into jailbreaking either immediately or at a follow-up malicious request. |
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)
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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
| Challenge: | Existing work deals with EL in the context of longer text, such as a sentence. |
| Approach: | They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches. |
| Outcome: | The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods. |
Learning to Paraphrase for Alignment with LLM Preference (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit the issue of paraphrase divergence, which means that when a question is phrased in a slightly different but semantically similar way, LLM may output a wrong response . retraining faces challenges in meeting the computational costs and privacy security demands of LLMs. |
| Approach: | They propose a black-box method that enhances model performance by paraphrasing questions in expressions preferred by the model. |
| Outcome: | The proposed method improves performance by paraphrasing questions in expressions preferred by the model. |
Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling (2025.acl-long)
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| Challenge: | Existing topic modeling models struggle in low-resource settings where data is limited . et al., 2003: domain adaptation for low-source topic modeling is challenging in low resources . |
| Approach: | They propose a domain adaptation framework that disentangles domaininvariant and domain-specific components to improve topic adaptation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on low-resource datasets on diverse datasets. |
Learning to Compress Prompt in Natural Language Formats (2024.naacl-long)
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| Challenge: | Existing work rely on compressing long contexts into soft prompts, but soft prompt compression encounters limitations in transferability . natural language (NL) prompts are incompatible with back-propagation, and NL prompts lack flexibility in imposing length constraints. |
| Approach: | They propose a framework that compresses long prompts into NL formatted Capsule Prompts. |
| Outcome: | The proposed framework reduces 81.4% of the original length, decreases inference latency up to 4.5x, and saves 80.1% of budget overheads while providing transferability across diverse LLMs and different datasets. |
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP (2021.emnlp-main)
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| Challenge: | We study whether and how cross-task generalization ability can be acquired . we use CrossFit to standardize seen/unseen task partitions and evaluation protocols . |
| Approach: | They propose a problem setup for studying cross-task generalization ability which standardizes seen/unseen task partitions and data access during different learning stages. |
| Outcome: | The proposed model can be used to build few-shot learners across diverse tasks. |
Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Existing fine-tuning algorithms for vision-language models are restricted by patient privacy concerns and can contain imperceptible noise. |
| Approach: | They propose a framework to mitigate adversarial noise and mitigate upstream noise during fine-tuning. |
| Outcome: | The proposed framework improves model robustness and transferability while decreasing noise levels negatively impact downstream performance. |
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)
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| Challenge: | Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask. |
| Approach: | They propose a new paradigm for universal information extraction that is compatible with any schema format and applicable to a list of IE tasks. |
| Outcome: | The proposed framework outperforms generative universal IE models on 14 benchmarks with the supervised setting and the state-of-the-art performance in low-resource scenarios. |
Distract Large Language Models for Automatic Jailbreak Attack (2024.emnlp-main)
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| Challenge: | Commercial large language models (LLMs) have made great progress in various NLP tasks. |
| Approach: | They propose a black-box jailbreak framework for automated red teaming of Large language models using an iterative optimization algorithm to conceal malicious content and memory reframing. |
| Outcome: | The proposed framework outperforms existing jailbreak defense methods and highlights the need to develop more effective and practical defense strategies. |
FacLens: Transferable Probe for Foreseeing Non-Factuality in Fact-Seeking Question Answering of Large Language Models (2025.emnlp-main)
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| Challenge: | Existing non-factuality detection methods require response generation, which incurs significant computational overhead. |
| Approach: | They propose a lightweight model called Factuality Lens which effectively probes hidden representations of fact-seeking questions for the NFP task. |
| Outcome: | The proposed model is able to probe hidden representations of fact-seeking questions and reduce development costs. |
Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL (2026.findings-acl)
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Zhaofeng Wu, Shiqi Wang, Boya Peng, Anuj Kumar Goyal, Melanie Kambadur, Sebastian Ruder, Yoon Kim, Chloe Bi
| Challenge: | Modern language models demonstrate impressive coding capabilities in common programming languages (PLs) but their performance in lower-resource PLs is often limited by training data availability. |
| Approach: | They propose a zero-shot cross-programming-language transfer task for code RL . they propose RL training in a source PL fails to improve performance on other target PLs . |
| Outcome: | The proposed approach improves transferability in Llama-3.1 code generation on parallel-stack model . it also improves performance on other target PLs, compared to single-PL SFT . |
TIGER: Text-Informed Generalized Enzyme-Reaction Retrieval (2026.acl-long)
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| Challenge: | Existing approaches to enzyme–reaction retrieval suffer from poor generalization across tasks and distributions . TIGER is a text-informed generalized enzyme-reaction retrieval framework that bridges enzymes and biochemical reactions. |
| Approach: | They propose a text-informed generalized enzyme-reaction retrieval framework that leverages protein-to-text generation models to distill textual knowledge from enzyme sequences. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in enzyme–reaction retrieval tasks and distributions. |
Chimera: Compositional Jailbreak Attacks on LLMs via Judgment-Driven Search over Heterogeneous Strategies (2026.findings-acl)
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| Challenge: | Existing methods for evaluating large language models face two limitations: they explore homogeneous transformations in isolation and rely on brittle judgment metrics that misclassify non-refusal hallucinations as successful attacks. |
| Approach: | They propose a framework that generates compositional jailbreak attacks via judgment-driven search over heterogeneous strategies. |
| Outcome: | The proposed framework generates compositional jailbreak attacks over heterogeneous strategies . strongREJECT++ improves attack success rates and transferability compared to state-of-the-art . |
HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs (2026.findings-acl)
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Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, Junbo Zhao
| Challenge: | High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. |
| Approach: | They define the first HSS domain system covering 14 mainstream fields and introduce HSS-Synth. |
| Outcome: | the proposed pipeline outperforms 14 leading baselines on 16 benchmarks. |
EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection (2026.acl-long)
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| Challenge: | Existing approaches to optimize tool-use policies are monolithic and prone to entangling behaviors. |
| Approach: | They propose a framework that decomposes agent’stool-use policy into four modules and improves them via three mechanisms. |
| Outcome: | The proposed framework outperforms strong baselines on bothGPT-4.1 and Qwen3-8B while maintaining superior efficiency and transferability. |
Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory (2026.acl-long)
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Derong Xu, Shuochen Liu, Pengfei Luo, Pengyue Jia, Yingyi Zhang, Yi Wen, Yimin Deng, Wenlin Zhang, Enhong Chen, Xiangyu Zhao, Tong Xu
| Challenge: | Existing memory systems rely on static, hand-crafted update rules for personalization, but sparse outcome rewards provide weak supervision, resulting in unstable long-horizon optimization. |
| Approach: | They propose a memory guideline optimization framework that learns how memory should be organized and what information to update. |
| Outcome: | The proposed framework learns how memory should be organized and what information to update. |