| Challenge: | Existing methods for generating semantically diverse sentences are based on locality-sensitive hash (LSH)-based semantic sentence codes that explicitly capture meaningful semantic differences. |
| Approach: | They propose a method for generating semantically diverse sentences using neural sequence-to-sequence models by conditioned on locality-sensitive hash-based semantic sentence codes whose Hamming distances correlate with human judgments of semantic textual similarity. |
| Outcome: | The proposed method improves output diversity without degrading performance on causal generation tasks. |
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| Challenge: | Large Language Models (LLMs) have shown proficiency in enhancing the generation quality across various tasks without the need for any fine-tuning. |
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Diverse Keyphrase Generation with Neural Unlikelihood Training (2020.coling-main)
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| Challenge: | Recent advances in neural natural language generation have made possible remarkable progress on the task of keyphrase generation, however, the importance of diversity in keyphrases has been largely ignored. |
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Transformer and seq2seq model for Paraphrase Generation (D19-56)
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| Challenge: | Existing methods for generating paraphrases fall into one of these broad categories -rule-based, seq2seq, deep generative models and a varied combination. |
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| Challenge: | Sequence-to-sequence (seq2sequ) models have a weakness: they cannot always generate sentences without grammatical errors. |
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Generating Diverse Translations with Sentence Codes (P19-1)
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| Challenge: | Existing methods to generate diverse translations use different sentence structures . Xu et al., 2018: generating multiple valid translations with high diversity is difficult . |
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Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries (2025.acl-long)
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| Challenge: | Large language models exhibit the _”lost in the middle” phenomenon when they are unevenly attending to different parts of the provided context. |
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Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (D18-1)
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Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
| Challenge: | a plethora of new natural language inference datasets has been created in recent years . however, these datasets do not provide clear insight into what type of reasoning or inference a model may be performing. |
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| Challenge: | Pretrained, large, generative language models have had great success in a wide range of sequence tagging and structured prediction tasks. |
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Exploring Diverse Expressions for Paraphrase Generation (D19-1)
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| Challenge: | Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. |
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