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In Tree Structure Should Sentence Be Generated

Yaguang Li, Xin Chen

TL;DR

This paper tackles limitations of autoregressive language generation, such as hallucinations and reasoning loops, by proposing a tree-structured generation approach in which sentences are built in binary-tree traversal order. It introduces SenTree, a learnable binary-tree converter, and a Transformer-SenTree framework that draws inspiration from diffusion models to emphasize heavier-weight words earlier in the generation process. A BERT-based structural probe enables extracting binary-tree representations, which are then converted into sequential text, with joint training aligning the transformer and tree generator. Empirically, the approach yields a BLEU improvement over a vanilla transformer on WMT 2014 English–German, suggesting that tree-structured generation can enhance translation quality and robustness, with potential extension to other modalities through GAN-like cooperative training.

Abstract

Generative models reliant on sequential autoregression have been at the forefront of language generation for an extensive period, particularly following the introduction of widely acclaimed transformers. Despite its excellent performance, there are always some issues that we face today. For example, problems such as hallucinations and getting trapped in a logic loop may occur. To enhance the performance of existing systems, this paper introduces a new method for generating sequences in natural language, which involves generating the targeted sentence in a tree-traversing order. The paper includes an illustration of the theoretical basis and validity of the approach, as well as a comparison of its fundamentals with the diffusion model in graphic generation. Finally, a module called SenTree is introduced for generating an approximating binary tree. It is already available at https://github.com/arklyg/sentree. Additionally, a joint training framework based on this approach is proposed, incorporating the intrinsics of generative adversarial networks.

In Tree Structure Should Sentence Be Generated

TL;DR

This paper tackles limitations of autoregressive language generation, such as hallucinations and reasoning loops, by proposing a tree-structured generation approach in which sentences are built in binary-tree traversal order. It introduces SenTree, a learnable binary-tree converter, and a Transformer-SenTree framework that draws inspiration from diffusion models to emphasize heavier-weight words earlier in the generation process. A BERT-based structural probe enables extracting binary-tree representations, which are then converted into sequential text, with joint training aligning the transformer and tree generator. Empirically, the approach yields a BLEU improvement over a vanilla transformer on WMT 2014 English–German, suggesting that tree-structured generation can enhance translation quality and robustness, with potential extension to other modalities through GAN-like cooperative training.

Abstract

Generative models reliant on sequential autoregression have been at the forefront of language generation for an extensive period, particularly following the introduction of widely acclaimed transformers. Despite its excellent performance, there are always some issues that we face today. For example, problems such as hallucinations and getting trapped in a logic loop may occur. To enhance the performance of existing systems, this paper introduces a new method for generating sequences in natural language, which involves generating the targeted sentence in a tree-traversing order. The paper includes an illustration of the theoretical basis and validity of the approach, as well as a comparison of its fundamentals with the diffusion model in graphic generation. Finally, a module called SenTree is introduced for generating an approximating binary tree. It is already available at https://github.com/arklyg/sentree. Additionally, a joint training framework based on this approach is proposed, incorporating the intrinsics of generative adversarial networks.
Paper Structure (15 sections, 9 equations, 7 figures, 1 table)

This paper contains 15 sections, 9 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Words with Weight
  4. Long-term Decay of Probability
  5. Defects Introduced by Long-term Decay of Probability
  6. Intrinsics
  7. Heavier Words First
  8. Binary Tree
  9. Correlations with Diffusion Model
  10. Methods
  11. Learnable Models rather than Fixed Algorithms
  12. Structural-Probe
  13. Process
  14. Experiments and Evaluation
  15. Conclusions and Future Work

Figures (7)

  • Figure 1: Long-term decay of position embedding (RoPE).
  • Figure 2: Adding noise process of diffusion model.
  • Figure 3: Tree generating process representing in spectra.
  • Figure 4: Structural tree of sentence.
  • Figure 5: Transformation by structural-probe based on BERT
  • ...and 2 more figures