Meta-Chunking: Learning Efficient Text Segmentation via Logical Perception

Thank you for your valuable feedback, which helps us improve the presentation of our paper.

W1: The contributions are overclaimed. From my viewpoint, marginal sampling chunking is similar to a simplification of the mentioned LumberChunker, which both prompts LLM to find chunking points. The only improvement is the design of a simpler prompt to reduce the language capability requirement, which is quite incremental.

• Our primary innovation lies in the introduction of the PPL Chunking, addressing the issues of prolonged runtime and high instruction-following capability requirements inherent in current LLMs chunking methodologies. Our research focuses on whether models of 7B or even smaller sizes can effectively and rapidly perform chunking. Given that LumberChunker is virtually inapplicable to models of 7B parameters or less, it becomes challenging to compare different methods within the same model scale. Consequently, we first propose Margin Sampling Chunking as a solution to alleviate the high instruction-following demands of the sota method, serving as an intermediary step to facilitate comparison with PPL Chunking.

• The structure of our previous paper led to some misunderstandings, prompting us to reorganize the methodology and main experimental sections. Additionally, we have relocated the content pertaining to Margin Sampling Chunking from the methodology section to the baseline section, aligning with the changes in the primary experiments. It is important to note that Margin Sampling Chunking is not merely an enhancement of the Prompt. To adapt the method for smaller models, we devised a margin sampling strategy to determine whether chunking should be performed. This differs from the LumberChunker approach, which relies on regular expression matching within answers generated by LLMs, thereby necessitating a high level of model instruction-following capability. In experiments, models of 7B parameters or less struggle with such complex tasks, resulting in chaotic outputs that ultimately impede the chunking process.

W2: For the PPL chunking, the authors do not clarify their specific design to locate the chucking points, which are quite complex in (3), but they do not have enough explanation or empirical experiment support.

This formula serves as a guide for the PPL Chunking to find minimum points of the PPL distribution: when the PPL on both sides of a point are higher than at that point, and the difference on at least one side exceeds the preset threshold $\theta$; or when the difference between the left point and the point is greater than $\theta$ and the right point equals the point value. These minima are regarded as potential chunk boundaries. We provide supplementary explanations for this in lines 182 to 185 of the paper. Since this formula is used to guide the PPL chunking method in identifying segmentation points, it is applied in all experiments related to PPL chunking, demonstrating the feasibility of this approach.

W3: The experiment presentation is confusing, the authors enumerate a lot of language models but many of them are missed in the PPL chunker rows. It should be recognized with different chunking methods applied to the same language model. Also, the performance of prompt-based chunkers is highly influenced by the prompt design while the PPL chunker is not. Given the rather similar performance between the two paradigms, the advantage of PPL chunker is questionable considering the prompt selection

• As stated in W1, we restructured the main results section to provide a clearer presentation of the PPL Chunking method, which addresses the limitations of current sota method that is either inapplicable to small models or suffer from long runtime. By employing three models of different scales, namely Qwen2-0.5B, Qwen2-1.5B, and Qwen2-7B, we investigated the performance and time consumption of various methods on these models.

• Compared to similarity-based chunking and LLMs chunking methods, PPL chunking maintains comparable performance while achieving faster speeds, making its runtime more acceptable in practical scenarios. Margin Sampling Chunking primarily addresses the high instruction-following capability requirements of the LumberChunker method. However, both of these LLMs chunking methods consume significant resources and time, posing challenges for practical applications. Although PPL Chunking is also a LLM method, its design enhances chunking speed, making it better suited for real-world scenarios involving massive text data.

Thank you for your constructive suggestions. We have refactored the methodology and main results sections of the original paper for clearer presentation.

W1: The performance of meta-chunking appears inconsistent. As shown in Table 1, margin sampling chunking methods often fall behind Llama index and similarity-based chunking, while requiring much more time, suggesting that margin sampling may be less effective.

Our main innovation is the introduction of the PPL Chunking method, which addresses the long runtime and high instruction-following capability requirements of current LLMs chunking methods. This allows LLMs chunking to be better applied in real-world scenarios with massive text. Since LumberChunker is almost unusable for models of 7B or smaller sizes, it is difficult to compare different methods under the same model. Therefore, we first propose Margin Sampling Chunking to address the high instruction-following capability requirements of the current sota method. As a minor improvement, it is included in the baseline to facilitate comparison with PPL Chunking, but its runtime is too long, similar to LumberChunker. PPL Chunking better addresses both of the aforementioned issues.

W2: Although this paper is titled 'efficient text segmentation via logical perception', the concept of 'logical perception' is unclear. Given that the authors use PPL for document segmentation, it is uncertain how this approach is logic-based.

In this paper, we categorize text chunking primarily into three types: rule-based, semantic similarity-based, and logic-based. Their instances can be observed through Figure 1 and Figure 2, and their specific differences are as follows:

• Rule-based: This method divides long texts into paragraphs of fixed length or combines sentences into specific text chunks based on specific rules. However, this approach does not consider the text content and is difficult to adapt to dynamic text changes, resulting in partitioning results that may not align with the actual context.

• Semantic similarity-based: This approach goes beyond relying solely on the surface structure or fixed rules of the text. Instead, it aims to deeply understand the text content and determine segmentation positions by calculating the semantic similarity between sentences. Nevertheless, the relationship between two sentences is not limited to similarity; there may also be progression, transition, and other contexts. As illustrated in Figure 1, two sentences with a progressive relationship may have low similarity, but they are logically connected and should be grouped together. Characterizing such relationships through semantic similarity alone can be challenging, potentially leading to their separation.

• Logic-based: LLMs exhibit robust logical reasoning capabilities. Whether it involves utilizing prompts to assess the logical relationship between two sentences or computing the perplexity variations across consecutive sentences, these models' reasoning abilities can be harnessed to capture deeper linguistic logic among sentences. This actually facilitates more effective guidance for text chunking.

W1: The paper's motivation could be better articulated. The authors seem to overlook the fundamental discussion of why text chunking is necessary in the first place, and don't clearly identify the specific limitations of existing chunking methods. The significance of the chunking phase within the RAG pipeline deserves more thorough examination

Thank you for your thoughtful questions. We have made improvements and enhancements based on the original paper.

1. When introducing the problem, we discussed the necessity of RAG and the limitations of current chunking in the pipeline, but did not address why chunking is needed. To this end, we have added additional explanations in lines 47-52 of the introduction section.

   • By delicately splitting long documents into multiple chunks, this module not only significantly improves the processing efficiency and performance of the system, reducing the consumption of computing resources, but also enhances the accuracy of retrieval [1]. Meanwhile, the chunking strategy allows information to be more concentrated, minimizing the interference of irrelevant information, enabling LLMs to focus more on the specific content of each text chunk and generate more precise responses [2].

2. Regarding the specific limitations of existing chunking methods, we have made comparisons in multiple places in the paper. By further clarifying, we hope to address your concerns and provide you with a clear understanding of shortcomings of current chunking methods.

   • In lines 52-83 of the introduction section, we highlight the shortcomings of current dynamic chunking methods and provide an example illustration through Figure 1. Currently, semantic similarity chunking cannot deeply capture the logical relationships between sentences, while LLMs chunking methods are slow and computationally expensive, making them difficult to adapt to real-world scenarios with massive text.

   • In lines 156-160 of the methodology section, we compare our method with existing methods while elaborating on our approach, and provide a visual demonstration in Figure 2. As can be seen, current methods either directly split the sentence or fail to capture the logical connections between sentences.

   • Additionally, we have moved the related work section from the end to the beginning to better provide relevant background knowledge.

[1] Maciej Besta, et al. Multi-head rag: Solving multi-aspect problems with llms. arXiv preprint arXiv:2406.05085, 2024.

[2] Weihang Su, et al. Dragin: Dynamic retrieval augmented generation based on the real-time information needs of large language models. arXiv preprint arXiv:2403.10081, 2024.

W2: The logic behind Table 1 is somewhat questionable. Why weren't different methods compared using the same LLM......whether the text chunking method might show more significant improvements with smallerLLM......

1. Thank you for highlighting this point. Since LumberChunker is almost inapplicable to models of 7B or smaller sizes, and we controlled variables to ensure consistent chunk lengths across different chunking methods for the same dataset, which was difficult for some baselines to adapt to, we were unable to fully demonstrate the results. Therefore, we reconstructed main results and migrated the content about Margin Sampling Chunking from the method section to the baseline section to align with the changes in the main experiment.

   • Our main innovation is the PPL Chunking method, which addresses issues of long runtime and high instruction-following requirements of current LLMs methods. Margin Sampling Chunking solves the high instruction-following capability requirement of the current sota method, but its runtime is still too long. Therefore, it is included as a minor improvement in the baseline for comparison with PPL Chunking.

   • Meanwhile, we explored the performance and time consumption of different methods on three models of different scales: Qwen2-0.5B, Qwen2-1.5B, and Qwen2-7B.

2. By adding experiments with the 0.5B model, we further explored the question of whether using a smaller model could bring more significant improvements to RAG.

   • We found that on the 2WikiMultihopQA and MultiFieldQA-zh datasets, chunking with the 0.5B model performed better than the 1.5B model. In other cases, the performance decreased slightly, but it was still better than the baseline. This confirms the flexibility of PPL Chunking, indicating that it does not have strict requirements on model performance.

   • Moreover, as the model scale decreases, the execution time for text chunking tasks decreases significantly, reflecting the advantage of small models in improving processing efficiency. This is consistent with our motivation to use small language models to solve the text chunking problem.

We sincerely hope this response clarifies your concerns. If you have any further questions, we would be happy to discuss them in more detail.