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Large Language Model

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/dice-group/Convolutional-Complex-Knowledge-Graph-Embeddings

공식 코드

Convolutional Complex Knowledge Graph Embeddings

https://github.com/dice-group/Leopard

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Knowledge Graph Validation

https://github.com/dice-group/MST5

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MST5 -- Multilingual Question Answering over Knowledge Graphs

https://github.com/dice-group/NLIWOD

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No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

https://github.com/dice-group/OOV-In-Link-Prediction

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Out-of-Vocabulary Entities in Link Prediction

https://github.com/dice-group/PYKE

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A Physical Embedding Model for Knowledge Graphs

https://github.com/dice-group/aswa

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Adaptive Stochastic Weight Averaging

https://github.com/dice-group/dice-embeddings

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Kronecker Decomposition for Knowledge Graph Embeddings

https://github.com/dice-group/evolearner

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EvoLearner: Learning Description Logics with Evolutionary Algorithms

https://github.com/dice-group/hybridfc

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HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs

https://github.com/dice-group/learnalclengths

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Learning Concept Lengths Accelerates Concept Learning in ALC

https://github.com/dice-group/litcqd

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LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals

https://github.com/dice-group/lola

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LOLA -- An Open-Source Massively Multilingual Large Language Model

https://github.com/dice-group/neuralclassexpressionsynthesis

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Neural Class Expression Synthesis

https://github.com/dice-group/universal_embeddings

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Universal Knowledge Graph Embeddings

https://github.com/dids-ei/llm-hbtp

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HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning

https://github.com/diegoemilio01/sparql-generation-with-entity-pre-trained-gpt-for-kg-question-answering

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SPARQL Generation with Entity Pre-trained GPT for KG Question Answering

https://github.com/diegogosmar/hall_evaluator

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Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks

https://github.com/digitalharborfoundation/rag-for-math-qa

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Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference

https://github.com/dilab-kaist/erbench

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ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models

https://github.com/dilab-zju/self-speculative-decoding

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Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding

https://github.com/diligmic/rcbm-neurips2024

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Relational Concept Bottleneck Models

https://github.com/dinobby/magdi

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MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models

https://github.com/dipuk0506/multimodal

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Batch Augmentation with Unimodal Fine-tuning for Multimodal Learning

https://github.com/discoveryanalyticscenter/enerex

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Lessons from Deep Learning applied to Scholarly Information Extraction: What Works, What Doesn't, and Future Directions

https://github.com/disi-unibo-nlp/rag-when-how-chunk

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Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation

https://github.com/disl-lab/finesumfact

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Learning to Verify Summary Facts with Fine-Grained LLM Feedback

https://github.com/ditec-project/semantic-association-rule-learning

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Learning Semantic Association Rules from Internet of Things Data

https://github.com/dkalpakchi/swectrl-mini

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SweCTRL-Mini: a data-transparent Transformer-based large language model for controllable text generation in Swedish

https://github.com/dlr-sc/style-vectors-for-steering-llms

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Style Vectors for Steering Generative Large Language Model

https://github.com/dlut-lab-zmn/mtdm

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Temporal Knowledge Graph Reasoning Triggered by Memories

https://github.com/dlyuangod/tinygpt-v

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TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

https://github.com/dmis-lab/rag2

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Rationale-Guided Retrieval Augmented Generation for Medical Question Answering

https://github.com/dmis-lab/self-biorag

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Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models

https://github.com/docailab/xrag

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XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation

https://github.com/dodo47/SpikE

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SpikE: spike-based embeddings for multi-relational graph data

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.