Papers with greedy search
Gender bias amplification during Speed-Quality optimization in Neural Machine Translation (2021.acl-short)
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| Challenge: | et al., 2002) show that gendered noun translation performance degrades faster than BLEU. |
| Approach: | They propose to use greedy search, quantization, AANs and shallow decoders to speed up decoding . they find minimal degradation of BLEU, but gendered noun translation degrades faster . |
| Outcome: | The proposed model degrades gendered noun translation performance faster than other models. |
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization (2025.emnlp-main)
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Ruoxi Cheng, Yizhong Ding, Shuirong Cao, Ranjie Duan, Xiaoshuang Jia, Shaowei Yuan, Simeng Qin, Zhiqiang Wang, Xiaojun Jia
| Challenge: | Existing methods to jailbreak Large Vision Language Models do not consider interaction between images and text. |
| Approach: | They propose a prior-guided bimodal interactive black-box jailbreak attack for toxicity maximization that exploits the interaction of images and text. |
| Outcome: | The proposed method outperforms state-of-the-art jailbreak methods in black box scenarios and in closed-source LVLMs. |
A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)
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| Challenge: | Neural text generation is notorious for repetitive loops and tedious outputs. |
| Approach: | They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text . |
| Outcome: | The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality. |
Latent Part-of-Speech Sequences for Neural Machine Translation (D19-1)
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| Challenge: | Existing methods for learning target side syntactic structure are greedy and only allow them to explore a limited portion of the latent space. |
| Approach: | They propose a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics while allowing for effective inference over the latent space. |
| Outcome: | The proposed model captures the co-dependence between syntax and semantics while allowing for efficient inference over the latent space. |
Searching for Search Errors in Neural Morphological Inflection (2021.eacl-main)
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| Challenge: | Neural sequence-to-sequence models are the predominant choice for language generation tasks. |
| Approach: | They find that on word-level tasks, the empty string is often the global optimum . they suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation. |
| Outcome: | The results suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation. |
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training (2025.acl-long)
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| Challenge: | Existing MGT detectors are vulnerable to simple perturbations and adversarial attacks. |
| Approach: | They propose an adversarial framework for training a robust machine-generated text detector called GREedy Adversary PromoTed DefendER. |
| Outcome: | The proposed framework reduces the Attack Success Rate (ASR) by 0.67% compared with SOTA defense methods. |
Adaptive Immune-based Sound-Shape Code Substitution for Adversarial Chinese Text Attacks (2024.emnlp-main)
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| Challenge: | Existing text attack methods are designed for English text, but robust implementation of Chinese text is understudied. |
| Approach: | They propose an adaptive immune-based sound-shape code algorithm for Chinese text attacks . they leverage the Sound-Shape Code to generate natural substitutions . |
| Outcome: | The proposed algorithm produces high-quality Chinese adversarial examples . it can reduce duplication of population and improve search ability . |
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation (D18-1)
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| Challenge: | Beam search is widely used in neural machine translation, but beam sizes larger than 5 hurt translation quality. |
| Approach: | They propose to use beam search to improve translation quality by using hyperparameter-free methods that outperform the widely-used heuristic of length normalization by +2.0 BLEU. |
| Outcome: | The proposed methods outperform the widely-used heuristic on Chinese-to-English translation and achieve the best results among all methods. |
TABS: Efficient Textual Adversarial Attack for Pre-trained NL Code Model Using Semantic Beam Search (2022.emnlp-main)
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| Challenge: | Existing black-box adversarial attacks on pre-trained models generate adversarials with greedy search. |
| Approach: | They propose an efficient beam search black-box adversarial attack method . they use contextual semantic filtering to effectively reduce the search space . |
| Outcome: | The proposed method shows good performance in terms of attack success rate, number of queries, and semantic similarity for two tasks: NL code search classification and retrieval tasks. |
Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models (2023.emnlp-main)
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| Challenge: | Keyphrase generation is a longstanding task in NLP with widespread applications. |
| Approach: | They propose a likelihood-based decode-select algorithm for seq2seq PLMs that improves greedy search by an average of 4.7% semantic F1 across five datasets. |
| Outcome: | The proposed algorithm improves greedy search by an average of 4.7% semantic F1 across five datasets. |
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks (2022.coling-1)
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| Challenge: | Neural networks are vulnerable to adversarial examples, i.e., under a black-box scenario. |
| Approach: | They propose a word-level search algorithm that searches for subareas under dynamic search space following the subarea importance. |
| Outcome: | The proposed algorithm can achieve comparable success rates to complex search methods while saving numerous queries and time. |
Consistency of a Recurrent Language Model With Respect to Incomplete Decoding (2020.emnlp-main)
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| Challenge: | Neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition. |
| Approach: | They propose to use a recurrent language model to address inconsistency in decoding algorithms that are inconsistent despite the fact that recursive language models are trained to produce sequences of finite length. |
| Outcome: | The proposed methods prevent inconsistency in the proposed models. |
Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation models are usually trained with word-level loss under teacher forcing algorithm . however, this method suffers from exposure bias due to high variance of gradient estimation . |
| Approach: | They propose a method with a differentiable sequence-level training objective . they use greedy search to alleviate the problem of exposure bias . |
| Outcome: | Experiments on Chinese-to-English translation tasks show that the proposed method outperforms the reinforcement-based methods. |