« Requête enrichie par graphes de connaissances » : différence entre les versions


Aucun résumé des modifications
Aucun résumé des modifications
Ligne 5 : Ligne 5 :


== Français ==
== Français ==
''' enrichissement par graphes de connaissances '''
''' requête enrichie par graphes de connaissances '''


== Anglais ==
== Anglais ==
''' Chain-of-Knowledge'''
''' Chain-of-Knowledge prompting'''


''' Chain-of-Knowledge framework '''
''' Chain-of-Knowledge framework prompting '''


The CHAIN-OF-KNOWLEDGE framework has two main components: dataset construction and model learning. For dataset construction, the authors first mine compositional rules from knowledge graphs. These rules represent patterns of how different facts can be combined to infer new knowledge. They then select knowledge triples from the graph that match these rules. Finally, they use advanced language models to transform the structured knowledge into natural language questions and reasoning steps.
''' CoK promptig '''
 
<!-- The CHAIN-OF-KNOWLEDGE framework has two main components: dataset construction and model learning. For dataset construction, the authors first mine compositional rules from knowledge graphs. These rules represent patterns of how different facts can be combined to infer new knowledge. They then select knowledge triples from the graph that match these rules. Finally, they use advanced language models to transform the structured knowledge into natural language questions and reasoning steps.
    
    
  For model learning, they initially tried simply fine-tuning LLMs on this data. However, this led to "rule overfitting" where models would apply rules even without supporting facts. To address this, they introduced a trial-and-error mechanism. This simulates how humans explore their internal knowledge when reasoning, by having the model try different rules and backtrack if it lacks key facts.
  For model learning, they initially tried simply fine-tuning LLMs on this data. However, this led to "rule overfitting" where models would apply rules even without supporting facts. To address this, they introduced a trial-and-error mechanism. This simulates how humans explore their internal knowledge when reasoning, by having the model try different rules and backtrack if it lacks key facts.
-->


== Source ==
== Source ==
[https://arxiv.org/pdf/2407.00653 Source : Zhang et al. 2024]


[https://huggingface.co/papers/2407.00653? Source : huggingface]
[https://cobusgreyling.medium.com/chain-of-knowledge-prompting-0285ac879ede Source : Medium]




[[Catégorie:vocabulary]]
[[Catégorie:Publication]]

Version du 24 septembre 2024 à 14:47

en construction

Définition

XXXXXXXXX

Français

requête enrichie par graphes de connaissances

Anglais

Chain-of-Knowledge prompting

Chain-of-Knowledge framework prompting

CoK promptig


Source

Source : Zhang et al. 2024

Source : Medium