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Knowledge Graphs

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하위 태스크: 5
평가표: 4
보존된 평가 기록: 19

A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.

코드

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

https://github.com/szeighami/nudge

공식 코드

NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval

https://github.com/t-sciq/t-sciq

공식 코드

T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering

https://github.com/taco-group/drama-x

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DRAMA-X: A Fine-grained Intent Prediction and Risk Reasoning Benchmark For Driving

https://github.com/tae2hee/coqah

공식 코드

Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model

https://github.com/taegyeong-lee/makd

공식 코드

Multi-aspect Knowledge Distillation with Large Language Model

https://github.com/taeheej/4bit-quantization-in-vector-embedding-for-rag

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4bit-Quantization in Vector-Embedding for RAG

https://github.com/taeheej/lightweight-relevance-grader-in-rag

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Lightweight Relevance Grader in RAG

https://github.com/tag-research/tag-bench

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Text2SQL is Not Enough: Unifying AI and Databases with TAG

https://github.com/taichengguo/llm_multiagents_survey_papers

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Large Language Model based Multi-Agents: A Survey of Progress and Challenges

https://github.com/tamsiuhin/kracl

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KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion

https://github.com/tan92hl/complex-question-answering-evaluation-of-chatgpt

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Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family

https://github.com/tan92hl/complex-question-answering-evaluation-of-gpt-family

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Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family

https://github.com/tanay2001/core

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CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation

https://github.com/tanchongmin/arc-challenge

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Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge

https://github.com/tanghulu6/llama3-8b-mob

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Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction

https://github.com/tangqiaoyu/selfretrieval

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Self-Retrieval: End-to-End Information Retrieval with One Large Language Model

https://github.com/tanny411/trutheval

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TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability

https://github.com/tanqiujiang/rapid

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RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models

https://github.com/tanwimallick/chatvis

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ChatVis: Automating Scientific Visualization with a Large Language Model

https://github.com/tanyuqian/knowledge-harvest-from-lms

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BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

https://github.com/taokz/FedR

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Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation

https://github.com/taowu974/leam

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LEAM: A Prompt-only Large Language Model-enabled Antenna Modeling Method

https://github.com/tapilot-crossing/tapilot_code

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Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents

https://github.com/tatsu-lab/alpaca_farm

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Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment

https://github.com/taugroup/thinktank

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ThinkTank: A Framework for Generalizing Domain-Specific AI Agent Systems into Universal Collaborative Intelligence Platforms

https://github.com/tayebiarasteh/radiorag

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RadioRAG: Factual large language models for enhanced diagnostics in radiology using online retrieval augmented generation

https://github.com/taylorwwebb/emergent_analogies_llm

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Emergent Analogical Reasoning in Large Language Models

https://github.com/tbenst/silent_speech

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A Cross-Modal Approach to Silent Speech with LLM-Enhanced Recognition

https://github.com/tclresearcheurope/edgewisepersona

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EdgeWisePersona: A Dataset for On-Device User Profiling from Natural Language Interactions

https://github.com/tdross/dna-llm

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Chaining thoughts and LLMs to learn DNA structural biophysics

https://github.com/teachable-ai-lab/tutor_gym

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TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students

https://github.com/teamnlp/typed-rag

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Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid Question Answering

https://github.com/teddy-xionggz/medrag

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Benchmarking Retrieval-Augmented Generation for Medicine

https://github.com/teddy-xionggz/mirage

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Benchmarking Retrieval-Augmented Generation for Medicine

https://github.com/tedfoley/nanoGPT

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LLM-e Guess: Can LLMs Capabilities Advance Without Hardware Progress?

https://github.com/tedzhouhk/sedyt

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SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings

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