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--- |
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license: apache-2.0 |
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task_categories: |
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- question-answering |
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language: |
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- en |
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tags: |
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- finance |
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- music |
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- medical |
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- food |
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- academic disciplines |
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- natural disasters |
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- software |
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- synthetic |
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pretty_name: Using KGs to test knowledge consistency in LLMs |
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size_categories: |
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- 10K<n<100K |
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--- |
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## What it is: |
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Each dataset in this delivery is made up of query clusters that test an aspect of the consistency of the LLM knowledge about a particular domain. All the questions in each |
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cluster are meant to be answered either 'yes' or 'no'. When the answers vary within a cluster, the knowledge is said to be inconsistent. When all the questions in a cluster |
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are answered 'no' when the expected answer is 'yes' (or viceversa), the knowledge is said to be 'incomplete' (i.e., maybe the LLM wasn't trained in that particular domain). |
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It is our experience that incomplete clusters are very few (less than 3%) meaning that the LLMs we have tested know about the domains included here (see below for a list of the |
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individual datasets), as opposed to inconsistent clusters, which can be between 6%-20% of the total clusters. |
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The image below indicates the types of edges the query clusters are supposed to test. It is worth noting that these correspond to common sense axioms about conceptualization, like |
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the fact that subConceptOf is transitive (4) or that subconcepts inherit the properties of their parent concepts (5). These axioms are listed in the accompanying paper (see below) |
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## How it is made: |
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The questions and clusters are automatically generated from a knowledge graph from seed concepts and properties. In our case, we have used Wikidata, |
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a well known knowledge graph. The result is an RDF/OWL subgraph that can be queried and reasoned over using Semantic Web technology. |
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The figure below summarizes the steps used. The last two steps refer to a possible use case for this dataset, including using in-context learning to improve the |
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performance of the dataset. |
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## Types of query clusters |
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There are different types of query clusters depending on what aspect of the knowledge graph and its deductive closure they capture: |
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Edge clusters test a single edge using different questions. For example, to test the edge ('orthopedic pediatric surgeon', IsA, 'orthopedic surgeon), the positive |
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or 'edge_yes' (expected answer is 'yes') cluster is: |
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"is 'orthopedic pediatric surgeon' a subconcept of 'orthopedic surgeon' ?", |
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"is 'orthopedic pediatric surgeon' a type of 'orthopedic surgeon' ?", |
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"is every kind of 'orthopedic pediatric surgeon' also a kind of 'orthopedic surgeon' ?", |
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"is 'orthopedic pediatric surgeon' a subcategory of 'orthopedic surgeon' ?" |
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There are also inverse edge clusters (with questions like "is 'orthopedic surgeon' a subconcept of 'orthopedic pediatric surgeon' ?") and negative or 'edge_no' clusters |
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(with questions like "is 'orthopedic pediatric surgeon' a subconcept of 'dermatologist' ?") |
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Hierarchy clusters measure the consistency of a given path, including n-hop virtual edges (in graph's the deductive closure). For example, the path |
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('orthopedic surgeon', 'surgeon', 'medical specialist', 'medical occupation') is tested by the cluster below |
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"is 'orthopedic surgeon' a subconcept of 'surgeon' ?", |
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"is 'orthopedic surgeon' a type of 'surgeon' ?", |
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"is every kind of 'orthopedic surgeon' also a kind of 'surgeon' ?", |
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"is 'orthopedic surgeon' a subcategory of 'surgeon' ?", |
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"is 'orthopedic surgeon' a subconcept of 'medical specialist' ?", |
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"is 'orthopedic surgeon' a type of 'medical specialist' ?", |
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"is every kind of 'orthopedic surgeon' also a kind of 'medical specialist' ?", |
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"is 'orthopedic surgeon' a subcategory of 'medical specialist' ?", |
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"is 'orthopedic surgeon' a subconcept of 'medical_occupation' ?", |
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"is 'orthopedic surgeon' a type of 'medical_occupation' ?", |
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"is every kind of 'orthopedic surgeon' also a kind of 'medical_occupation' ?", |
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"is 'orthopedic surgeon' a subcategory of 'medical_occupation' ?" |
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Property inheritance clusters test the most basic property of conceptualization. If an orthopedic surgeon is a type of surgeon, we expect that |
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all the properties of surgeons, e.g., having to be board certified, having attended medical school or working on the field of surgery, are inherited by orthopedic surgeons. |
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The example below tests the later: |
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"is 'orthopedic surgeon' a subconcept of 'surgeon' ?", |
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"is 'orthopedic surgeon' a type of 'surgeon' ?", |
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"is every kind of 'orthopedic surgeon' also a kind of 'surgeon' ?", |
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"is 'orthopedic surgeon' a subcategory of 'surgeon' ?", |
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"is the following statement true? 'orthopedic surgeon works on the field of surgery' ", |
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"is the following statement true? 'surgeon works on the field of surgery' ", |
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"is it accurate to say that 'orthopedic surgeon works on the field of surgery'? ", |
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"is it accurate to say that 'surgeon works on the field of surgery'? " |
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## List of datasets |
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To show the versatility of our approach, we have constructed similar datasets in the domains below. We test one property inheritance per dataset. The Wikidata main QNode |
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(the node corresponding to the entities) and PNode (the node corresponding to the property) are indicated in parenthesis. |
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| domain | top concept | WD concept | main property | WD property | |
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|----- | ----- | -----| ----- | ----- | |
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| Academic Disciplines | "Academic Discipline" | https://www.wikidata.org/wiki/Q11862829 | "has use" | https://www.wikidata.org/wiki/Property:P366 | |
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| Dishes | "Dish" | https://www.wikidata.org/wiki/Q746549 | "has parts" | https://www.wikidata.org/wiki/Property:P527 | |
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| Financial products | "Financial product" | https://www.wikidata.org/wiki/Q15809678 | "used by" | https://www.wikidata.org/wiki/Property:P1535 | |
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| Home appliances | "Home appliance" | https://www.wikidata.org/wiki/Q212920 | "has use" | https://www.wikidata.org/wiki/Property:P366 | |
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| Medical specialties | "Medical specialty" | https://www.wikidata.org/wiki/Q930752 | "field of occupation" | https://www.wikidata.org/wiki/Property:P425 | |
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| Music genres | "Music genre" | https://www.wikidata.org/wiki/Q188451 | "practiced by" | https://www.wikidata.org/wiki/Property:P3095 | |
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| Natural disasters | "Natural disaster" | https://www.wikidata.org/wiki/Q8065 | "has cause" | https://www.wikidata.org/wiki/Property:P828 | |
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| Software | "Software" | https://www.wikidata.org/wiki/Q7397 | "studied in" | https://www.wikidata.org/wiki/Property:P7397 | |
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The size and configuration of the datasets is listed below |
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| domain | edges_yes | edges_no | edges_in | hierarchies | property hierarchies | |
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| ------------------- | :----: | :-----: | :-----: | :-----: | :-----: | |
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| Academic Disciplines | 52 | 308 | 52 | 30 | 1 | |
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| Dishes | 225 | 521 | 224 | 72 | 178 | |
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| Financial product | 112 | 433 | 108 | 40 | 32 | |
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| Home appliances | 58 | 261 | 58 | 31 | 13 | |
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| Medical specialties | 122 | 386 | 114 | 55 | 63 | |
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| Music genres | 490 | 807 | 488 | 212 | 139 | |
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| Natural disasters | 45 | 225 | 44 | 21 | 22 | |
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| Software | 80 | 572 | 79 | 114 | 4 | |
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## Want to know more? |
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For background and motivation on this dataset, please check https://arxiv.org/abs/2405.20163 Also to be published in COLM 2024, |
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@inproceedings{Uceda_2024_1, <br/> |
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  title={Reasoning about concepts with LLMs: Inconsistencies abound}, <br/> |
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  author={Rosario Uceda Sosa and Karthikeyan Natesan Ramamurthy and Maria Chang and Moninder Singh}, <br/> |
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  booktitle={Proc.\ 1st Conference on Language Modeling (COLM 24)}, <br/> |
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  year={2024} <br/> |
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} |
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## Questions? Comments? |
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Please contact rosariou@us.ibm.com, knatesa@us.ibm.com, Maria.Chang@ibm.com or moninder@us.ibm.com |
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