💬 NLP

ReLTEx: Reliable LLM-based Taxonomy Expansion

The paper introduces ReLTEx, a framework that enhances the reliability and semantic coherence of LLM-based taxonomy expansion by combining candidate generation with structure-aware validation and recursive expansion control to mitigate hallucinations and hierarchical inconsistencies.

Original authors: Zeinab Ghamlouch, Mehwish Alam

Published 2026-08-12

📖 7 min read🧠 Deep dive

CC BY 4.0

Original authors: Zeinab Ghamlouch, Mehwish Alam

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the internet as a giant, chaotic library where every book, idea, and fact is scattered across the floor. To make sense of this mess, scientists use something called a taxonomy. Think of a taxonomy as a super-organized filing cabinet or a family tree for ideas. It groups things together based on how they relate: "Dog" is a type of "Animal," and "Animal" is a type of "Living Thing." This structure helps computers understand the world, find answers to questions, and navigate through massive amounts of data. But here's the problem: the world changes fast. New ideas pop up every day, and trying to manually update these filing cabinets is like trying to sort a million Lego bricks by hand—it's slow, expensive, and impossible to keep up with.

Recently, we've met a new helper: the Large Language Model (LLM). You can think of an LLM as a super-smart, well-read robot that has read almost everything on the internet. It's great at guessing what comes next in a sentence or suggesting new ideas. Scientists hoped these robots could automatically build and update our filing cabinets. But there's a catch. Sometimes, these robots get a little too creative. They might suggest that "River Bank" is a type of "Financial Institution" just because they share the word "Bank," or they might invent a concept that doesn't actually exist. If we let them run wild, our neat filing cabinet could turn into a messy pile of nonsense.

This is where a new study comes in, proposing a framework called ReLTEx. The researchers wanted to see if they could use these smart robots to expand our taxonomies without letting them make a mess. They didn't just let the robot write whatever it wanted; they built a "quality control" system to check every single suggestion. They found that by combining the robot's creativity with a strict, structure-aware checker, they could generate new, useful concepts that actually fit into the family tree correctly. The study suggests that this method produces much more reliable results than just letting the robot guess on its own, though it notes that the system still needs to be tested on even larger and more complex datasets to be truly perfect.

The Story of ReLTEx: Taming the Creative Robot

So, how does ReLTEx actually work? Imagine you are the head librarian of a very strict library. You have a robot assistant who is incredibly good at writing stories and inventing new characters. You ask the robot, "What kind of animals live in the ocean?" The robot might say, "Fish, whales, and... a 'Sea-Cheese'!"

If you just took the robot's word for it, your library would be full of fake animals. ReLTEx is the system you build to stop this. It works in three main steps, like a three-stage assembly line for ideas.

Step 1: The Creative Spark
First, the system asks the robot (the LLM) to come up with new ideas. But it doesn't just say "give me ideas." It gives the robot a specific context. It shows the robot the path from the top of the library down to the current shelf. For example, if the robot is looking at the "Fruit" section, it sees that "Fruit" is under "Food," and it sees that "Apple" and "Banana" are already there. The robot is told to invent new fruits that fit this level of detail. It might suggest "Mango" or "Kiwi." The robot generates a list of candidates, but we know it might also suggest "Spoon" or "Blue" by mistake.

Step 2: The Strict Inspector
This is the most important part. Before any new idea gets put on the shelf, it has to pass a test. The researchers built a special "inspector" (a classifier) that acts like a strict librarian. This inspector doesn't just check if the words sound nice; it checks the structure. It asks: "Does this new idea actually belong under this parent?"

The inspector was trained on a huge list of correct and incorrect examples. It learned that "Answer" should go under "Question," but "Answer" should not go under "Creative Work" just because answers can be creative. It also learned to spot "hallucinations"—ideas the robot made up that don't exist. If the robot suggests "Sea-Cheese," the inspector says, "Nope, that's not a real animal, and it doesn't fit the family tree." Only the ideas that pass this strict test are allowed to stay.

Step 3: The Safety Brake
Sometimes, a robot might get one idea right and then get carried away, inventing a whole chain of nonsense based on that one mistake. ReLTEx has a safety brake for this. It looks at how confident the inspector was about the new ideas. If the inspector is only 50% sure about the new "Sea-Cheese," the system stops expanding that branch. It says, "We aren't sure enough here, so let's stop before we make a bigger mess." This ensures that the library doesn't grow in the wrong direction.

What They Found

The researchers tested this system on two different "libraries" (datasets). One was a smaller, standard test set called SemEval-2016 Task 13 Environment, and the other was a massive, real-world library called Schema.org with over 1,100 concepts.

They played a game called "Masked Taxonomy Expansion." Imagine they hid some of the real books in the library and asked the robot to find them again.

On the smaller library, the robot Mistral (one of the models they tested) managed to find about 42% of the hidden books exactly right.
On the giant Schema.org library, it found about 23%.

While these numbers might sound low, the researchers note that this is actually a very hard task because the robot has to invent new ideas from scratch, not just pick from a list. More importantly, when they asked human experts to look at the results, the experts were very impressed. For the Schema.org library, the human judges gave the system a near-perfect score of 1.000 for "Granularity Consistency" (meaning the new ideas fit perfectly at the right level of detail) and 1.000 for "Hierarchy Relationship Rationality" (meaning the parent-child relationships made logical sense).

The study suggests that ReLTEx is a big step forward because it doesn't just rely on the robot's "gut feeling." By adding the structural inspector, the system filters out the nonsense and keeps the family tree organized.

The Limits and the Future

The researchers are careful to point out that this isn't a magic wand that solves everything. They used relatively small, open-source robots for their tests, and they suspect that bigger, more powerful robots might do even better. They also admit that their "inspector" is a learned approximation, meaning it might still occasionally let a wrong idea slip through.

Furthermore, because this is a new way of doing things (generating ideas instead of just sorting existing ones), there isn't a perfect standard way to compare it to old methods yet. The study suggests that while ReLTEx produces more reliable and coherent taxonomies, it's still an emerging field. The team concludes that their approach offers a promising way to keep our digital libraries organized in a world that changes faster than we can manually update them, but it's a tool that needs to be used with care and continued testing.

Technical Summary: ReLTEx – Reliable LLM-based Taxonomy Expansion

1. Problem Formulation

Taxonomies provide structured hierarchical representations of knowledge essential for applications like knowledge graphs and semantic search. However, manual maintenance is costly and difficult to scale as domains evolve. While automated taxonomy expansion has been explored using lexico-syntactic patterns, distributional semantics, and Pretrained Language Models (PLMs), existing methods face significant limitations:

Candidate Constraints: Most approaches operate under a constrained setting where candidate concepts are provided in advance, rather than generating new concepts dynamically.
LLM Hallucinations and Inconsistencies: Direct reliance on Large Language Models (LLMs) for generation often results in noisy, redundant, or hierarchically inconsistent structures. Generated concepts may be semantically ambiguous, redundant (e.g., "Question" vs. "Inquiry"), or attached to inappropriate parents, compromising hierarchical coherence.
Recursive Propagation: Without explicit structural verification, errors in parent-child relations can accumulate and propagate during recursive expansion, leading to semantic drift.

The core problem addressed is how to perform generative taxonomy expansion—creating entirely new concepts dynamically—while ensuring the resulting structure remains semantically coherent and hierarchically consistent.

2. Methodology: The ReLTEx Framework

ReLTEx is a framework designed to combine zero-shot LLM-based candidate generation with structure-aware validation and recursive expansion control. The process operates in three distinct stages:

A. LLM-based Candidate Generation

The framework performs a depth-first traversal of the seed taxonomy. For each target node, it constructs a prompt containing:

Local Hierarchical Context: The path from the root to the target node.
Existing Children: To guide the Level of Abstraction (granularity).

The LLM is instructed to generate a fixed number ( $k$ ) of new child concepts, avoiding duplicates, synonyms, or simple rephrasings of existing siblings. The generated candidates undergo basic lexical normalization to remove exact duplicates before validation.

B. Structure-Aware Candidate Validation

To filter hallucinated or structurally inconsistent relations, ReLTEx employs a classifier-guided validation mechanism. Unlike methods relying solely on semantic similarity, this module learns structural compatibility.

Model: A DistilRoBERTa encoder fine-tuned on labeled parent-child relation pairs.
Input Format: Path [SEP] Parent [SEP] Child, where the path represents the ancestor sequence.
Training Data:
- Positive Examples: Valid taxonomy edges from the seed.
- Negative Examples: "Hard negatives" generated via hierarchy-aware perturbations, including reversed edges, sibling confusions, grandparent-child confusions, same-depth mismatches, nearby hierarchy confusions, and random invalid relations.
Decision: The classifier outputs a confidence score $s(p, v, c_i)$ . Candidates with scores above a threshold $\tau$ are accepted; others are discarded.

C. Recursive Expansion Control

To prevent the propagation of errors and uncontrolled growth, ReLTEx implements a recursive stopping mechanism based on the confidence scores of accepted children:

Minimum Count: A branch only continues if it has at least $m$ accepted children ( $|A_v| \ge m$ ).
Confidence Stability: Expansion continues only if the mean confidence of the current level's accepted children ( $S(v)$ ) does not drop significantly below the parent's mean confidence ($S(parent(v))$). Specifically, $S(v) \ge S(parent(v)) - \delta$ .

3. Key Contributions

The paper proposes three primary contributions:

A Zero-Shot Expansion Framework: A system that exploits contextual information from the taxonomy hierarchy to generate candidate concepts without relying on a predefined candidate pool.
Structure-Aware Validation: A mechanism using a trained classifier to mitigate hallucinations and structurally inconsistent parent-child relations, specifically designed to improve the reliability of recursive expansion.
Comprehensive Evaluation Protocol: A multi-faceted evaluation strategy combining adapted automatic metrics, human assessment, and LLM-based taxonomy evaluation (using LITE).

4. Experimental Results

The framework was evaluated on two benchmarks: SemEval-2016 Task 13 (Environment) and Schema.org, using a masked taxonomy expansion setting where 20% of leaf nodes were hidden as ground truth. Four open-source LLMs (Llama3.2, Mistral, Qwen3, DeepSeek-R1) were tested as generators.

Quantitative Metrics

Recovery: All models demonstrated the ability to recover hidden concepts. Mistral achieved the strongest overall performance on the larger Schema.org dataset (highest R@K, MRR, and WuP scores), while Llama3.2 performed best on the smaller SemEval dataset.
Semantic vs. Lexical: Qwen3 showed strong semantic recovery capabilities, achieving high SoftRecall scores, indicating an ability to generate semantically equivalent concepts even without exact lexical matches.
Stability: DeepSeek-R1 exhibited lower recovery scores and higher variance, suggesting less stability across different masking configurations.

Human Evaluation

Annotators evaluated generated expansions on Hierarchical Correctness (HC), Granularity Consistency (GC), and Non-Redundancy (NR).

Results showed consistently high scores across all models for HC, GC, and NR, indicating that accepted concepts are generally well-positioned and consistent with the abstraction level of the branch.
Qwen3 achieved the highest HC and GC on SemEval, while Mistral and Qwen3 tied for high Semantic Recovery (SR).

LLM-Based Evaluation (LITE)

Using the LITE framework to assess the fully expanded taxonomies (recursive expansion):

Semantic Quality (SCA): Enriched taxonomies maintained high SCA scores, indicating recursive expansion preserves semantic clarity.
Logical Coherence (HRR): Hierarchy Relationship Rationality was generally maintained or improved.
Exclusivity/Independence (HRE/HRI): Most evaluators assigned equal or higher scores to enriched taxonomies, particularly on the Environment dataset.

5. Significance and Claims

The paper claims that ReLTEx addresses the critical gap in automated taxonomy expansion by moving beyond the "candidate attachment" paradigm to generative expansion with built-in reliability checks.

Reliability: By validating each parent-child relation in its hierarchical context before insertion, the framework reduces the accumulation of errors common in recursive LLM generation.
Quality: The combination of structure-aware validation and recursive stopping criteria produces taxonomies that are not only semantically coherent but also hierarchically consistent.
Evaluation: The work establishes a more rigorous evaluation protocol that accounts for the unique challenges of generative expansion (e.g., semantic variants, alternative parent nodes) through adapted metrics and multi-perspective assessment (human and LLM).

Limitations Acknowledged

The authors note that the experiments rely on relatively compact open-source models; larger models might yield higher quality. Additionally, the classifier is a learned approximation and may occasionally accept incorrect relations. Finally, the field lacks a standardized benchmark for recursive generative expansion, making direct system-level comparisons difficult.