A Theoretical Analysis of the Repetition Problem in Text Generation (2012.14660v4)

Abstract: Text generation tasks, including translation, summarization, LLMs, and etc. see rapid growth during recent years. Despite the remarkable achievements, the repetition problem has been observed in nearly all text generation models undermining the generation performance extensively. To solve the repetition problem, many methods have been proposed, but there is no existing theoretical analysis to show why this problem happens and how it is resolved. In this paper, we propose a new framework for theoretical analysis for the repetition problem. We first define the Average Repetition Probability (ARP) to characterize the repetition problem quantitatively. Then, we conduct an extensive analysis of the Markov generation model and derive several upper bounds of the average repetition probability with intuitive understanding. We show that most of the existing methods are essentially minimizing the upper bounds explicitly or implicitly. Grounded on our theory, we show that the repetition problem is, unfortunately, caused by the traits of our language itself. One major reason is attributed to the fact that there exist too many words predicting the same word as the subsequent word with high probability. Consequently, it is easy to go back to that word and form repetitions and we dub it as the high inflow problem. Furthermore, we derive a concentration bound of the average repetition probability for a general generation model. Finally, based on the theoretical upper bounds, we propose a novel rebalanced encoding approach to alleviate the high inflow problem. The experimental results show that our theoretical framework is applicable in general generation models and our proposed rebalanced encoding approach alleviates the repetition problem significantly. The source code of this paper can be obtained from https://github.com/fuzihaofzh/repetition-problem-nlg.

• The paper introduces a novel framework by defining the Average Repetition Probability (ARP) to quantitatively assess repetition in generated text.
• It employs both Markov and generalized models with matrix theory to derive upper bounds on repetition, highlighting linguistic inflow issues.
• The study proposes a rebalanced encoding method that effectively reduces repetition in neural machine translation and language modeling tasks.

A Theoretical Analysis of the Repetition Problem in Text Generation

The paper "A Theoretical Analysis of the Repetition Problem in Text Generation" by Zihao Fu et al. develops a robust theoretical framework to address the prevalent repetition problem in text generation systems. Despite significant advancements in text generation, the issue of repetition remains a challenge that degrades the performance of models used in tasks such as translation, summarization, and LLMing. This paper seeks to fill the gap in theoretical understanding by elucidating the causes of this problem and proposing potential mitigation strategies.

Key Contributions

The authors introduce a novel analytical framework that defines the repetition problem quantitatively. They propose the Average Repetition Probability (ARP), which serves as a formal metric to quantify repetition in generated text. This framework is utilized to derive upper bounds for ARP, providing insights into how current mitigation strategies implicitly or explicitly address repetition.

The research highlights the linguistic traits that contribute to repetition, notably the "high inflow problem," whereby too many words predict the same subsequent word with high probability. This insight underlines the inherent complexity of natural languages, where certain linguistic constructs naturally lead to repeated phrases or structures.

Theoretical Framework and Analysis

The paper’s framework is centered on two generative models: the Markov generation model and a generalized model extending to more complex scenarios. The Markov model, simplified to ease analysis, acts as a base to investigate the roots of repetition by generating text based solely on the previous word. By contrast, the general generation model incorporates broader contexts, reflecting more realistic generative systems.

The authors derive ARP upper bounds using principles of matrix theory, notably leveraging the properties of stochastic matrices that characterize word transition probabilities. This rigorous approach reveals that current sampling strategies, including Temperature and Topk sampling, successfully mitigate repetition by minimizing the variance in the transition matrix, thus reducing the likelihood of generating repetitive text.

Practical Implications and Rebalanced Encoding

Building on these insights, the authors propose a rebalanced encoding (RE) method designed to address high inflow probabilities. This innovative approach involves merging high inflow word pairs into single units, effectively reducing both the inflow and variances that contribute to repetitions.

Experiments conducted on neural machine translation (NMT) and LLMing (LM) tasks provide empirical support for the theoretical framework. The RE method consistently reduces repetition metrics, showcasing its potential as a more effective solution compared to existing techniques.

Future Directions

The paper lays a foundation for further exploration into the systemic linguistic causes of repetition and potential encoding-based solutions. Future research can build on these findings by integrating the theoretical insights with advanced model architectures, enhancing the robustness of text generation systems against repetition.

Additionally, the theoretical framework could be expanded to include deeper analyses of context-driven repetition in multi-turn dialogues or more complex, content-driven generation tasks. By understanding and mitigating the root causes of repetition, more nuanced and less predictable text generation is achievable, enhancing the utility of AI in natural language processing applications.

In summary, this paper presents a well-defined theoretical framework addressing the pervasive repetition problem in text generation, introducing innovative solutions that promise to enhance system performance across various applications.