Text Tiling Done Right: Building Solid Foundations For Your Personal LLM | by Massimiliano Costacurta | Jun, 2023

How to build a text tiling model from scratch using both semantic and lexical similarity.

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Photo by Gary Butterfield on Unsplash

It’s like everybody’s trying to get their own Large Language Model (LLM) these days, tweaking it to work on their private collection of documents. The privacy factor plays a big role here, amplifying the demand for more private GPT models. However, the journey to crafting a personal chatbot isn’t straightforward and you basically have two main options to do that.

Firstly, you could build a custom question-answer dataset from scratch and use it to fine-tune your LLM. But let’s face it, this isn’t a feasible option for most of us due to the high costs and significant time commitment it requires. An alternative, more affordable approach involves generating context on the fly. This is done by retrieving relevant sections from your documents based on the user’s query, with the help of embeddings. While there’s no shortage of tutorials explaining how to do this, few highlight the critical importance of appropriately chunking, or ‘tiling’, your documents.

Here’s why it’s essential: if your document tiles aren’t well cut, your context could be off, leading your LLM to provide answers that either completely miss the point or, worse, generate false information — a phenomenon often referred to as ‘hallucination’ in machine learning. This is where the art of text tiling comes into play. It’s all about breaking down a document into coherent, meaningful chunks that can facilitate precise, relevant context retrieval. In doing so, you’re likely to improve the overall performance of your LLM, making it more adept at understanding queries and providing accurate responses.

Now, it may surprise you (just like it surprised me) to know that in the world of Python programming, there aren’t many options for text tiling. Our primary tool available is nltk.tokenize.texttiling, which is not even very well documented. Realizing this lack of variety and the potential for improvement, I’ve decided to embark on developing my own text-tiling model, leveraging the revolutionary technologies offered by Natural Language Processing (NLP) and transformers.

Whenever I embark on developing a new model, I always try and begin with the end in mind, then work backwards from there. In this case, our “end” is assessing the output of our model. Without a means of evaluation, we can’t measure performance and thus can’t make improvements. For this reason, creating an evaluation mechanism is essential before even attempting to develop a model. Evaluating text tiling, however, poses unique challenges because it relates to the topics found within the document(s) at hand. This presents us with two major hurdles:

  1. We don’t have a dataset of documents along with their corresponding tiling.
  2. Even if we had such a dataset, it would be exceptionally difficult to utilize, given that partitioning a document by topic is highly subjective.

To navigate these issues, we’ll adopt a straightforward approach: create a synthetic document. This document will be a concatenation of diverse documents, which ensures we know the exact thresholds separating the original documents. These thresholds should be identified by our model. In this article, I’ll employ one document as an example (which can be found here). Still, this same methodology could be applied to assemble a multitude of documents for a comprehensive model test. The composite document is crafted from the concatenation of the following Medium articles (to their respective authors, consider this a bit of free promotion, you can thank me later 😀):

Now that we’ve established a method to evaluate our model, we need to define how to measure its efficacy. Let’s first consider our synthetic document, which has pre-known, clear thresholds. In our case, these thresholds are: (0, 56, 74, 118, 163). This implies that the first article concludes after 56 sentences, the second after 74, and so forth. Our model will likely output a similar yet more detailed list of thresholds based on the subtopics it identifies within each article. An example output might be: (0, 26, 54, 67, 74, 90, 112, 120, 130, 163).

So, how do we evaluate our model’s effectiveness? The most logical method I could devise involves computing a sort of “edit distance” between the original vector and the model’s output. This process works as follows:

  1. Identify all the numbers in the model’s output that are closer to the true thresholds (excluding the first and the last ones). Using the example above we would end up with (54, 74, 120).
  2. If there are fewer numbers than the true thresholds, fill the gap with ‘None’ (not happening in our example).
  3. Compute the distance between each corresponding threshold, substituting the maximum value of the original vector whenever you encounter a ‘None’. In our case that would generate (2, 0, 2).
  4. Sum these distances and normalize by dividing by the maximum threshold of the original vector multiplied by the vector’s length. This provides a distance ranging from 0 to 1 that can be easily transformed into a score by computing: 1-distance. In our case: 1-4/163 → 1-0,0245 → 0,975
  5. (Optional) Impose a penalty on the score depending on the total number of thresholds. This is designed to penalize models that generate too many thresholds. Although such thresholds are statistically likely to be close to the real ones, they might not necessarily be meaningful.

Here is the code implementing the scoring computation as described in the preceding steps.

This function, as it currently stands, is far from perfect. Specifically, it tends to produce values that skew towards 1. However, it will suffice for our purpose of comparing the results generated by our model. If you have any suggestions for improving its implementation, I would be more than happy to hear them in the comments.

Now that we’ve established a method to assess our model’s performance, we can begin to consider how to extract the thresholds. The underlying concept of our model is quite straightforward: the tiles of our document are essentially clusters of sentences exhibiting some level of similarity. Put differently, pairs of sentences within the same tile should yield a high similarity score, while pairs of sentences from different tiles should yield a low one. Regardless of the clustering method we’ll decide to use, we can safely say that we need a similarity measure for the sentences. In NLP, the two most prevalent forms of similarity measures are lexical (based on word comparison) and semantic (based on meaning, and more technically on embeddings). In our model, we will test different types of similarity scores and compare their performance on our test document. Specifically, we will utilize the following models/algorithms:

The function that computes the similarity scores is essentially a large “IF-ELSE” block of code, switching between different approaches based on the input model selected by the user.

For performance optimization, this function accepts two lists of sentences as input and returns the corresponding list of similarity measures.

Next, we need to decide which similarity scores we actually want to calculate. One approach could be to compute pairwise similarities by comparing each sentence with all others. This method, while comprehensive, is not only inefficient and hardly scalable, but also undesirable. The clusters we’re seeking will consist of consecutive sentences, meaning that we aren’t interested in identifying connections between sentences that are far apart in the document — in fact, we’re aiming to avoid this! At the other extreme, we could consider computing similarity only between a given sentence and the one that follows it. While it’s reasonable to assume that adjacent sentences share the same meaning, this approach carries risks too. Consider ‘filling sentences’ — those used to embellish the text but don’t convey any specific meaning or contribute to the context. Examples include phrases like “I’ll try to say it differently” or “But I digress” (which appears in our test document!). Such sentences might create artificial thresholds that we certainly want to avoid.

We’ll therefore adopt a hybrid approach, where we only compute the similarity between each sentence and the K subsequent ones (yes, K is a hyperparameter). As illustrated in the figure below, once we’ve set the K parameter, each sentence will connect to 2*K sentences — K sentences preceding and K sentences following.

Image by author.

The code that generates the similarity scores in this manner is encapsulated within the function create_similarity_graph:

As the function name suggests, what this function constructs is a graph where the nodes represent sentences and the similarity scores serve as the weights of the edges. The output format is as follows:

(Sentence1, Sentence2, sim_ratio1)

(Sentence1, Sentence3, sim_ratio2)

(Sentence1, SentenceK, sim_ratioK-1)

(Sentence2, Sentence3, sim_ratioK)

Indeed, this is essentially a graph in the form of (parent, child, edge_weight). It’s also important to note the coefficient math.exp(-l/2) at line 27, which multiplies the similarity scores. We employ this coefficient to accommodate the “distance effect”—the idea that the similarity between two sentences should decrease the further apart they are.

With our similarity scores in hand, the next step is to find an effective way to cluster them. The graph structure of our current data suggests a suitable direction for selecting the right algorithm. There are numerous options for graph clustering, but I have a particular fondness for the Louvain method for community detection, mainly due to its ease of implementation in Python and its ability to efficiently handle large-scale data.

In the algorithm’s context, a “community” refers to a cluster of nodes densely interconnected, while sparsely connected with nodes from other communities. Translating this concept into our document tiling task, these “communities” represent the tiles we seek. Each tile, or community, is a cluster of highly related sentences forming a coherent topic or subtopic within the document.

Given the above graph structure, extracting communities is a matter of few lines of code.

While the Louvain algorithm excels at finding communities within our graph, it’s essential to remember that these communities may not always correspond to coherent sequences of sentences in the context of document tiling. This is because the algorithm is not inherently aware that it’s dealing with a textual document where sequence and continuity of sentences matter.

In the process of community detection, the Louvain algorithm might produce clusters that include non-sequential sentences. For example, it may generate a cluster like (1, 2, 3, 4, 6, 7), leaving out sentence 5. While this cluster may still have high internal similarity, it does not form a logical tile in the document, as there’s a “gap” or “jump” from sentence 4 to sentence 6. This is a crucial point to consider when applying graph-based clustering to document tiling. Our expectation is to find coherent, uninterrupted segments of text — tiles that represent contiguous blocks of related content.

To solve this issue, I incorporated a post-processing step in the code, specifically invoking the compact_clusters function at line 46. As I didn’t come across any pre-existing algorithm performing this task (if you know one, I’d be glad to hear about it), I devised a simple one based on the following steps:

  1. For each pair of clusters, identify the range of overlap between them. Considering clusters a=(1, 2, 3, 6, 7, 8) and b=(4, 5, 9, 10, 11), the range overlap would be (4, 5, 6, 7, 8).
  2. Determine the least number of movements required to eliminate the overlap. In our example, we could either transfer (6, 7, 8) from cluster ‘a’ to ‘b’, or move (4, 5) from cluster ‘b’ to ‘a’. The optimal choice is the latter, requiring fewer movements. In case of a tie, a random decision can be made.
  3. Reconfigure the clusters accordingly. Following this adjustment, we would have a = (1, 2, 3, 4, 5, 6, 7, 8) and b = (9, 10, 11).

This approach ensures that the final tiles we produce are not just coherent within themselves but also make sense in the context of the original document sequence.

Here is the implementation of the function:

Now that we have built all our essential functions, it’s a straightforward task to write a script that determines the tiles of our target document. It merely takes a few lines of code:

As previously mentioned, we’re testing only four models, but integrating additional ones is as easy as modifying the get_similarity_scores function by adding new ‘elif’ sections. Given the nature of our problem, it’s also quite insightful to construct a visual representation of the calculated tiles. This graphic depiction provides an immediate sense of how our algorithms are performing compared to the original document. Here is the plotting function I used:

And below is the resultant plot:

Image by author.

The first bar illustrates the original document, segmented into the four distinct articles that compose it. It’s evident that BERT Score performs impressively, perfectly matching all three primary thresholds, while paraphrase-MiniLM-L6-v2 misses 2 out of 3 (barely missing the second one, and significantly deviating on the third). It’s also noteworthy that the two semantic models identify quite similar subtopics within the articles, suggesting the potential to employ an ensemble approach for determining accurate thresholds in real-world applications. Surprisingly, the lexical models didn’t perform too poorly, although Jaccard did introduce spurious thresholds that don’t correlate with the document’s structure.

To sum up, here are the scores of the four tested algorithms:

  • BERT Score: 0.9950
  • paraphrase-MiniLM-L6-v2: 0.9321
  • SequenceMatcher: 0.9208
  • Jaccard: 0.9830

Based on our short test involving the target document, it’s clear that the proposed approach to document tiling exhibits significant promise. As we anticipated, the semantic approach shows an advantage over the lexical one for this specific task due to its ability to capture deeper, context-based relationships within the text. The repository with the working code explained in this article is available here. Possible areas of improvement include:

  1. Refinement of the Scoring Function: The current tiling score function exhibits a bias towards the value of 1. Addressing this bias to make the scoring function more balanced would improve the reliability of the results and provide a more accurate assessment of model performance.
  2. Exploration of Additional Models: We tested only four models in this study. Testing additional models, especially different types of semantic models, could reveal new insights and further improve performance. This could also include experimenting with ensemble approaches that combine the strengths of multiple models.
  3. Validation Across Multiple Documents: Our test involved just one document. Evaluating the performance of our approach over a variety of documents would give us a clearer picture of its robustness and generalizability. Different types of texts, genres, or topics could affect the performance of the tiling process.
  4. Enhancement of Subtopic Identification: Although our models identified subtopics within the articles, there is potential for refinement. An ensemble method or other advanced strategies could be used to improve the accuracy of subtopic determination, ensuring that the derived tiles reflect the nuanced structure of the document.

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