Papers with Bayesian optimization
Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization (2020.tacl-1)
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| Challenge: | Existing methods that focus on learning a ranking across the whole candidate space are lacking user or task-specific training data. |
| Approach: | They propose an interactive ranking approach that actively selects pairs of candidates, from which the user selects the best. |
| Outcome: | The proposed approach outperforms existing methods in community question answering and extractive multidocument summarization and is an effective reward function for reinforcement learning. |
A Bayesian Optimization Approach to Machine Translation Reranking (2025.naacl-long)
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| Challenge: | reranking is a method of improving prediction quality but can add computational cost. |
| Approach: | They propose to score a list of prediction candidates with an external scoring model and return the highest-scoring candidate. |
| Outcome: | The proposed method achieves the same CometKiwi score using 70 evaluations on average compared to scoring a subset of 180 candidates. |
QPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models (2025.findings-naacl)
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| Challenge: | Structured pruning can reduce model size but results in significant accuracy degradation . quantization and pruning increase the difficulty of fine-tuning, requiring a more refined quantization scheme. |
| Approach: | They propose a structured pruning framework followed by a layer-wise mixed-precision quantization scheme to reduce model memory consumption during fine-tuning and inference. |
| Outcome: | Experiments on benchmark datasets show that QPruner outperforms existing methods in memory savings while maintaining or improving model performance. |
Dynamic Fisher-weighted Model Merging via Bayesian Optimization (2025.naacl-long)
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| Challenge: | Existing merging approaches involve scaling the parameters model-wise or integrating parameter importance parameter-wise. |
| Approach: | They propose a method for merging model-based models at the parameter level without training data or joint training. |
| Outcome: | The proposed model merging framework outperforms baseline models on validation sets. |
CCSum: A Large-Scale and High-Quality Dataset for Abstractive News Summarization (2024.naacl-long)
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| Challenge: | Existing datasets for supervised news summarization contain considerable amount of noise and expensive training data. |
| Approach: | They propose a large-scale and high-quality dataset for supervised abstractive news summarization containing 1.3 million training samples. |
| Outcome: | The proposed dataset is more factual and informative than established summarization datasets. |
Data-scarce Behavior Editing of Language Models (2025.findings-emnlp)
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| Challenge: | Prior studies show that noisy neural circuitries coexist with generalizable abilities within LLMs. |
| Approach: | a new method is proposed to improve the generalizability of large-scale web-based text models . a TaRot method is based on learnable rotation matrices optimized for Bayesian optimization . |
| Outcome: | a new method for task adaptation improves on multiple classification and generation tasks . it improves upon zero- and few-shot performance, with average improvements of 14% and 15% . |
Query-Efficient Black-Box Red Teaming via Bayesian Optimization (2023.acl-long)
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| Challenge: | Existing methods for generating test cases and querying fail to be query-efficient . generative models can be used for open-domain dialogue, prompt continuation, text-to-image generation . |
| Approach: | They propose a query-efficient method that iteratively finds diverse positive test cases leading to model failures by utilizing user input and past evaluations. |
| Outcome: | The proposed method finds a significantly larger number of diverse positive test cases under limited query budget than baseline methods. |
Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models (2025.acl-long)
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| Challenge: | Understanding how jailbreaking works remains limited, hindering the development of effective defense strategies. |
| Approach: | They propose a new mechanism that adaptively constrains activations within the safety boundary and propose 'Activation Boundary Defense' to enhance its effectiveness. |
| Outcome: | The proposed defense achieves an average Defense Success Rate (DSR) of over 98% against various jailbreak attacks, with less than 2% impact on the model’s general capabilities. |
Composable Cross-prompt Essay Scoring by Merging Models (2025.emnlp-main)
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| Challenge: | Existing approaches to cross-prompt automated essay scoring use all available sources . however, using multiple sources for continual adaptation raises privacy concerns . |
| Approach: | They propose a source-free adaptation approach that selectively merges the parameters of individual models without further access to the source datasets. |
| Outcome: | The proposed method outperforms joint-training methods on all sources while maintaining computational efficiency. |
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 . |