GenAI

GenAI

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LLM Resource Hub
LLM Resource Hub
A comprehensive collection of Large Language Model (LLM) resources, tools, and learning materials.
ConceptConcept
·llmresourceshub.vercel.app·
LLM Resource Hub
NODES 2024 - A Graph Entity Resolution Playbook
NODES 2024 - A Graph Entity Resolution Playbook
Entity resolution, the process of determining which digital descriptions correspond to the same real-world entities, is an important graph use case. It is also a crucial precursor to many graph data science projects. In this session, you will learn steps that the Neo4j professional services team has used in many entity resolution projects. The steps include designing a graph data model that highlights shared identifiers, standardizing the format of node properties, identifying outlier nodes that should be excluded from the matching process, using graph data science algorithms to identify duplicate entities, using string similarity to identify misspellings, and capturing the results of entity resolution in your graph. Get certified with GraphAcademy: https://dev.neo4j.com/learngraph Neo4j AuraDB https://dev.neo4j.com/auradb Knowledge Graph Builder https://dev.neo4j.com/KGBuilder Neo4j GenAI https://dev.neo4j.com/graphrag
TutorialTutorial#entity-resolution#neo4j
·m.youtube.com·
NODES 2024 - A Graph Entity Resolution Playbook
LLM-Based Extraction of Contradictions from Patents
LLM-Based Extraction of Contradictions from Patents
Already since the 1950s TRIZ shows that patents and the technical contradictions they solve are an important source of inspiration for the development of innovative products. However, TRIZ is a heuristic based on a historic patent analysis and does not make use of...
Paper
·link.springer.com·
LLM-Based Extraction of Contradictions from Patents
Generating Prototypes for Contradiction Detection Using Large Language Models and Linguistic Rules | IEEE Conference Publication | IEEE Xplore
Generating Prototypes for Contradiction Detection Using Large Language Models and Linguistic Rules | IEEE Conference Publication | IEEE Xplore
We introduce a novel data generation method for contradiction detection, which leverages the generative power of large language models as well as linguistic rules. Our vision is to provide a condensed corpus of prototypical contradictions, allowing for in-depth linguistic analysis as well as efficient language model fine-tuning. To this end, we instruct the generative models to create contradicting statements with respect to descriptions of specific contradiction types. In addition, the model is also instructed to come up with completely new contradiction typologies. As an auxiliary approach, we use linguistic rules to construct simple contradictions such as those arising from negation, antonymy and numeric mismatch. We find that our methods yield promising results in terms of coherence and variety of the data. Further studies, as well as manual refinement are necessary to make use of this data in a machine learning setup.
10.1109/BigData59044.2023.10386499
Paper
·ieeexplore.ieee.org·
Generating Prototypes for Contradiction Detection Using Large Language Models and Linguistic Rules | IEEE Conference Publication | IEEE Xplore
Content
Content
Paper
·publica-rest.fraunhofer.de·
Content
Automated requirement contradiction detection through formal logic and LLMs - Automated Software Engineering
Automated requirement contradiction detection through formal logic and LLMs - Automated Software Engineering
This paper introduces ALICE (Automated Logic for Identifying Contradictions in Engineering), a novel automated contradiction detection system tailored for formal requirements expressed in controlled natural language. By integrating formal logic with advanced large language models (LLMs), ALICE represents a significant leap forward in identifying and classifying contradictions within requirements documents. Our methodology, grounded on an expanded taxonomy of contradictions, employs a decision tree model addressing seven critical questions to ascertain the presence and type of contradictions. A pivotal achievement of our research is demonstrated through a comparative study, where ALICE’s performance markedly surpasses that of an LLM-only approach by detecting 60% of all contradictions. ALICE achieves a higher accuracy and recall rate, showcasing its efficacy in processing real-world, complex requirement datasets. Furthermore, the successful application of ALICE to real-world datasets validates its practical applicability and scalability. This work not only advances the automated detection of contradictions in formal requirements but also sets a precedent for the application of AI in enhancing reasoning systems within product development. We advocate for ALICE’s scalability and adaptability, presenting it as a cornerstone for future endeavors in model customization and dataset labeling, thereby contributing a substantial foundation to requirements engineering.
Contradictory opposites, such as he is sick and he is not sick, are mutually exclusive; one must be true if the other is false, without overlap. Contrary opposites, like it is black and it is white, cannot be true at the same time but can both be false, indicating they are not exhaustive. Subaltern relation follows, where if everybody is sick is true, it implies some people are sick must also be true, demonstrating a logical step-down.
Simplex contradictions (from Latin ‘simple’) are characterized by direct opposition without conditional statements. These contradictions are straightforward but crucial for establishing our classification framework. For example, ‘The car must be red’ versus ‘The car must be blue’ showcases apparent, uncomplicated contradictions.
Idem contradictions (from Latin ‘same’) involve identical conditions leading to contradictory outcomes, presenting challenges due to their conditional nature. An example is ‘If the customer wishes, the car must be red’ and ‘If the customer wishes, the car must be blue,’ where the same condition yields conflicting requirements.
Alius contradictions (from Latin ‘different’), distinguished by differing conditions that result in incompatible conclusions, illustrate the complexity of engineering requirements. An instance of this is ‘If the customer wishes, the car must be red’ versus ‘If the car has four doors, the car must be blue,’ demonstrating how different conditions can lead to contradictory outcomes.
Paper
·link.springer.com·
Automated requirement contradiction detection through formal logic and LLMs - Automated Software Engineering
Agent Protocol: Interoperability for LLM agents
Agent Protocol: Interoperability for LLM agents
LangGraph is a multi-agent framework. This means not only interacting with other LangGraph agents, but all other types of agents as well, regardless of how they are built. Today we are taking a few steps to to build towards this vision. We are announcing: * Agent Protocol: a common interface for
ConceptConcept
·blog.langchain.dev·
Agent Protocol: Interoperability for LLM agents
SynaLinks/HybridAGI: The Programmable Cypher-based Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
SynaLinks/HybridAGI: The Programmable Cypher-based Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
The Programmable Cypher-based Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI
ToolTool
·github.com·
SynaLinks/HybridAGI: The Programmable Cypher-based Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
Rig - Build Powerful LLM Applications in Rust
Rig - Build Powerful LLM Applications in Rust
Rig: Build modular and scalable LLM Applications in Rust. Unified LLM interface, Rust-powered performance, and advanced AI workflow abstractions for efficient development.
PackagePackage
·rig.rs·
Rig - Build Powerful LLM Applications in Rust