Integrating Geodesic Interpolation and Flow Matching for Non-Autoregressive Text Generation in Logit Space

Authors: Egor Sevriugov, Ivan Oseledets
Primary area: generative models
Keywords: Flow Matching, Non-autoregressive text generation

Abstract:
Non-autoregressive language models are emerging as effective alternatives to autoregressive models in the field of natural language processing, facilitating simultaneous token generation. This study introduces a novel flow matching approach that employs Kullback-Leibler (KL) divergence geodesics to interpolate between initial and target distributions for discrete sequences. We formulate a loss function designed to maximize the conditional likelihood of discrete tokens and demonstrate that its maximizer corresponds to the flow matching velocity during logit interpolation. Although preliminary experiments conducted on the TinyStories dataset yielded suboptimal results, we propose an empirical sampling scheme based on a pretrained denoiser that significantly enhances performance. Additionally, we present a more general hybrid approach that achieves strong performance on more complex datasets, such as Fine Web and Lamini Instruction.

Decision: reject
Reviewer scores: 3, 5, 1

Reviews and discussion:
<thread>
creation date:
1729502898044

thread_type:
official_review

<thread_object>
creator: reviewer_94s6

<content>
summary:
This work presents a flow matching approach for generating discrete sequences. This approach treats discrete tokens as one-hot vectors and constructs a flow by interpolation on the logit space. Randomized top-k sampling is proposed for inference.

strengths:
N/A

weaknesses:
This paper is only half-baked and needs substantial refinements before resubmission. For example, the presentation is poor (many variables are not explained, Figure 1/2 have the same caption, and some references are placeholders), experiments are only conducted on toy datasets (Tiny Stories, MNIST), and evaluation metrics are not sound (only use generative perplexity for language modeling).

questions:
N/A
</content>
</thread_object>

</thread>
<thread>
creation date:
1730121825139

thread_type:
official_review

<thread_object>
creator: reviewer_cxed

<content>
summary:
The paper introduces a novel method for non-autoregressive text generation using KL-divergence geodesics and flow matching in logit space. The authors propose a conditional flow matching approach to address the challenges of discrete sequence modeling, demonstrating theoretical alignment between the loss function and flow matching velocity. To enhance performance, they implement an empirical sampling scheme based on a pretrained denoiser. Experiments on both text and image datasets show that the method outperforms traditional autoregressive models. Despite promising results, the sampling technique lacks full theoretical justification.

strengths:
- The paper introduces a novel application of KL-divergence geodesics for text generation, addressing limitations in linear interpolation commonly encountered in discrete sequence modeling.

- The use of a pretrained denoiser-based empirical sampling scheme demonstrates ingenuity, compensating for initial performance shortcomings and achieving improved generation results.

weaknesses:
- The paper seems to have been written in a hurry and lacks proper polish, with numerous missing references that make it difficult for me to follow. For example, references are missing at lines 32, 33, 39, 53, and 90, which disrupts the flow of the paper.
- I find the experimental section quite limited, as it only includes a single experiment for both text and image generation. A detailed ablation study is missing, making it hard to understand the impact of different components.
- I believe the evaluation metric for text generation is too restricted, relying almost exclusively on perplexity. While perplexity is useful for understanding how well the generated text fits the probable distribution, it can fail to capture semantic richness. I would recommend adding metrics like BLEU, ROUGE, or exploring newer evaluation methods for a more comprehensive assessment.
- After reading the introduction, I still do not fully understand why flow matching is necessary for generation models. The motivation for choosing this specific approach remains unclear to me.

questions:
See the Weaknesses part
</content>
</thread_object>

</thread>
<thread>
creation date:
1730559060726

thread_type:
official_review

<thread_object>
creator: reviewer_umbn

<content>
summary:
This paper presents a novel approach for non-autoregressive text generation in logit space. It uses Kullback-Leibler (KL) divergence geodesics for flow matching between initial and target distributions of discrete sequences. A loss function is defined to maximize the conditional likelihood of discrete tokens, and its theoretical properties are explored. Despite initial poor results on the TinyStories dataset, an empirical sampling scheme based on a pretrained denoiser is proposed, which significantly improves performance. The method is also applied to image generation tasks for comparison.

strengths:
1) Novel theoretical approach: The use of KL-divergence geodesics for flow matching in discrete sequence modeling is a novel concept. The theoretical justification provided for the likelihood function and its relation to the flow matching velocity adds to the rigor of the method.

weaknesses:
1) Extremely low writing quality: The writing and presentation of this article are extremely poor and unreasonable.

2) Limited dataset evaluation: The evaluation is conducted on two uncommon datasets.

questions:
Given the low quality of presentation, I have no further questions. I hope that the authors can make full preparations and improvements before the next submission.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732269288422

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
We conducted two additional experiments in which the Logit-FM method outperformed DFM and demonstrated comparable or superior results to the autoregressive model, enhancing the text in light of the new experiments and clarifying the mathematical motivation.

comment:
We would like to express our gratitude to the reviewers for their thorough consideration of our research work. In response to their comments, we conducted two additional experiments that are detailed in the revised manuscript:

1) We trained a model on sequences of length 1024, utilizing 10 billion tokens from the Fine Web dataset, which consists of cleaned and deduplicated English web data (refer to Table 2 in the revision).

2) We also trained a model for the conditional language modeling task employing the Lamini Instruction dataset, comprised of 2.58 million pairs of instructions and responses, with a sequence length of 512 (see Table 3 in the revision).

In both experiments, the Logit-FM method consistently outperformed the DFM approach and demonstrated performance that is either superior to or comparable with that of the autoregressive model. For the evaluation of the model on the Lamini Instruction dataset, we utilized several metrics, including ROUGE-L, BERT Score, and BLEU Score.

The text has been revised to incorporate new experiments (Tables 2 and 3) and results analysis. Additionally, the mathematical motivation behind the randomized inference approach has been clarified. Furthermore, Appendices A and B include an investigation of the optimal FM training configuration and hyperparameters for the inference procedure.
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper introduces a novel non-autoregressive text generation method using Kullback-Leibler (KL) divergence geodesics for flow matching in logit space. It proposes a conditional flow matching approach to address challenges in discrete sequence modeling and define a loss function to maximize the conditional likelihood of discrete tokens.  However, all reviewers consistently vote for rejection due to the paper seems  half-baked and should be further improved by carefully polishing in writing and adding more experiments to demonstrate their method.