Papers with APT
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning (2023.acl-short)
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| Challenge: | Parameter-efficient fine-tuning only optimizes a few task-specific parameters with frozen pre-trained model. |
| Approach: | They propose to optimize a prefix vector inserted into Transformer layers to optimize the prefix . they propose to use a gate mechanism to adjust the prefixed to each layer . |
| Outcome: | The proposed approach improves on the SuperGLUE and NER datasets. |
What Would a Teacher Do? Predicting Future Talk Moves (2021.findings-acl)
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| Challenge: | Recent advances in natural language processing (NLP) have the ability to transform how classroom learning takes place. |
| Approach: | They propose a task that uses the academically productive talk framework to learn strategies that make for the best learning experience. |
| Outcome: | The proposed task outperforms baselines on academically productive talk (FTMP) and shows that it outperformed human performance on FTMP. |
Towards Human Understanding of Paraphrase Types in Large Language Models (2025.coling-main)
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| Challenge: | Current paraphrase evaluations of language models use binary approaches, offering limited interpretability of specific text changes. |
| Approach: | They introduce a dataset of 800 sentence-level and word-level annotations by 15 annotators and a human preference ranking of paraphrases with different types. |
| Outcome: | The proposed model can generate simple APTs, but struggle with complex structures (e.g., subordination changes). |
Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point (D18-1)
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| Challenge: | a range of issues limit the performance of the automated metrics. |
| Approach: | They propose to use semi-automatic metrics and test suites instead of fully automatic metrics for pronoun translation. |
| Outcome: | The proposed metrics improve translation accuracy by comparing them against a manually annotated dataset . the proposed metrics are semi-automatic and test suites in place of fully automatic metrics. |
Improving Paraphrase Detection with the Adversarial Paraphrasing Task (2021.acl-long)
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| Challenge: | a new adversarial method of paraphrase identification is being used to identify paraphrases based on word overlap and syntax . authors propose a dataset that generates semantically equivalent but lexically and syntactically disparate paraphrase pairs . |
| Approach: | They propose an adversarial method for paraphrase identification that uses word overlap and syntax to identify paraphrases. |
| Outcome: | The proposed method improves paraphrase detection accuracy and speed of generation of datasets. |
Domain Generalizable AI Guardrails with Augmented Policy Training (2026.acl-long)
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| Challenge: | Current guardrails overfit the training policies, preventing adaptation to new domains and policies. |
| Approach: | They propose a training recipe that uses a suite of policy perturbation strategies to reduce overfitting and increase generalization to guardrails. |
| Outcome: | The proposed training recipe reduces overfitting and increases generalization on unseen policies and achieves comparable or better performance than existing 8B guardrails on unsen policies. |
APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training (2025.findings-acl)
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| Challenge: | Large Language Models often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities. |
| Approach: | They propose to use self-generated dis-preferred weakness data to enhance model performance with a targeted training approach that minimizes interference with existing knowledge base. |
| Outcome: | The proposed approach ensures no reduction in generic capacity and achieves superior performance on downstream tasks compared to existing methods. |
Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models (2026.acl-long)
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Zirui Song, Qian Jiang, Mingxuan Cui, Mingzhe Li, Lang Gao, Zeyu Zhang, Zixiang Xu, Yanbo Wang, Guangxian Ouyang, Zhenhao Chen, Xiuying Chen
| Challenge: | a recent study evaluated large audio-language models against jailbreak attacks . a new benchmark is being developed to evaluate LAM safety against jailbreaking attacks based on temporal and semantic nature of speech . |
| Approach: | They propose a benchmark to evaluate LAM jailbreak vulnerabilities in adversarial audio prompts . they use a dataset of 1,495 adversarials to evaluate their performance . |
| Outcome: | The proposed benchmark evaluates state-of-the-art LAMs against jailbreak attacks . it demonstrates that even small, semantically preserved perturbations can reduce safety . |
N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator (2026.acl-long)
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| Challenge: | Evaluating the safety robustness of LLMs is critical for their deployment. |
| Approach: | They propose to use latent representations to characterize hidden layer dynamics by analyzing the APT of latent models and introducing the JSS metric. |
| Outcome: | The proposed method exploits the APT (Angular-Probabilistic Trajectory) of latent representations and introduces the JSS (Jensen-Shannon Separability) metric. |