| Challenge: | Existing studies have focused on general response generation with neural network-based approaches, but none have addressed specific types of repetitions. |
| Approach: | They propose a weighted label smoothing method for explicitly learning which words to repeat during fine-tuning and a repetition scoring method that can output more appropriate repetitions during decoding. |
| Outcome: | The proposed method outperforms baselines in automatic and human evaluations on a pre-trained language model for generating repetitions. |
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| Challenge: | Existing studies on text generation with LLMs focus on attention heads, framing repetition as a key mechanism for in-context learning. |
| Approach: | They introduce repetition neurons that are regarded as "skill neurons" responsible for the repetition problem in text generation tasks. |
| Outcome: | The authors identify repetition neurons as "skill neurons" that perceive repetition as a task to copy the previous context repeatedly, similar to in-context learning. |
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning (2021.eacl-main)
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| Challenge: | Existing approaches to language modeling use autoregressive methods, but they can produce repetitive results. |
| Approach: | They propose to add a loss function for regularization to avoid unwanted properties, such as contradiction or repetition, to a language model by using policy gradient reinforcement learning. |
| Outcome: | The proposed method reduces repetition without impacting the language model quality. |
Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method (D18-1)
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| Challenge: | Experimental results show that Sequence-to-sequence models tend to generate generic/dull responses . |
| Approach: | They propose a statistical re-weighting method that assigns different weights for multiple responses of the same query. |
| Outcome: | The proposed method improves acceptance rate of generated responses and significantly reduces generated generic responses. |
Self-Repetition in Abstractive Neural Summarizers (2022.aacl-short)
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| Challenge: | a quantitative and qualitative analysis of self-repetition in neural summarization systems is needed to improve training. |
| Approach: | They propose a quantitative and qualitative analysis of self-repetition in neural summarization systems . they analyze the behavior of three popular architectures fine-tuned on five datasets . |
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A Systematic Review of Reproducibility Research in Natural Language Processing (2021.eacl-main)
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| Challenge: | Despite the recent progress in reproducibility, the field is far from reaching a consensus on how reproducibility should be defined, measured and addressed. |
| Approach: | They propose to provide a wide-angle snapshot of current work on reproducibility in NLP. |
| Outcome: | The proposed work will provide a wide-angle snapshot of current work on reproducibility in NLP. |
Mitigating the Learning Bias towards Repetition by Self-Contrastive Training for Open-Ended Generation (2023.findings-acl)
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| Challenge: | Existing language models generate repetitive texts with greedy decoding or beam search. |
| Approach: | They propose a self-contrastive training technique to penalize the output of a premature checkpoint of the same model when it incorrectly predicts repetition. |
| Outcome: | The proposed training mitigates repetition while maintaining fluency while minimizing the overestimation of token-level repetition probabilities. |
Penalty Decoding: Well Suppress the Self-Reinforcement Effect in Open-Ended Text Generation (2023.emnlp-main)
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| Challenge: | Experimental results demonstrate the efficacy of our approach in generating high-quality sentences resembling human output. |
| Approach: | They propose a forgetting mechanism that disregards distant tokens, reducing the burden of penalty selection. |
| Outcome: | The proposed approach generates high-quality sentences resembling human output. |
Rethinking Repetition Problems of LLMs in Code Generation (2025.acl-long)
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| Challenge: | Recent studies have focused on content repetition, but structural repetition is a more prevalent problem in code generation. |
| Approach: | They propose a decoding approach that eliminates repetition problems in code generation by identifying grammar rules and strategically decaying the likelihood of critical tokens that contribute to repetitions. |
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Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation (D18-1)
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| Challenge: | Neural text generation has been quite successful recently, but during training time, only one reference is considered for each example, even though there are often multiple references available. |
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Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (NMT) uses a sequence-to-sequence model to generate synthetic data. |
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