Large Language Model
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/scunlp/bar
공식 코드BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks
https://github.com/scut-dlvclab/tonggu-llm
공식 코드TongGu: Mastering Classical Chinese Understanding with Knowledge-Grounded Large Language Models
https://github.com/scxsunchenxi/test
공식 코드TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
https://github.com/sdamirsa/llm-vlm-in-gastroenterology
공식 코드https://github.com/sding11/RAG-VR
공식 코드RAG-VR: Leveraging Retrieval-Augmented Generation for 3D Question Answering in VR Environments
https://github.com/sdm-tib/eablock
공식 코드EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines
https://github.com/se7esx/eib
공식 코드https://github.com/sebastian-steindl/CoPrUS_data
공식 코드CoPrUS: Consistency Preserving Utterance Synthesis towards more realistic benchmark dialogues
https://github.com/sebischair/kg-conv-exploratory-search
공식 코드https://github.com/sebischair/kg-in-nlp-survey
공식 코드A Decade of Knowledge Graphs in Natural Language Processing: A Survey
https://github.com/sebischair/llm-kg-d2t
공식 코드A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation
https://github.com/sebischair/llm-sp-cqa
공식 코드https://github.com/sec-bench/sec-bench
공식 코드SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks
https://github.com/seeyourmind/ptbox
공식 코드https://github.com/selbaez/evaluating-conversations-as-ekg
공식 코드Evaluating Agent Interactions Through Episodic Knowledge Graphs
https://github.com/selbeltagy/arragexperiments
공식 코드https://github.com/semantic-systems/elenglishwd
공식 코드https://github.com/semantic-systems/nfdi-search-engine-chatbot
공식 코드Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
https://github.com/semanto-mondal/A-RAG-Augmented-YOLOv8-Framework
공식 코드https://github.com/seohyunwoo-0407/gar
공식 코드https://github.com/sepermetric/seper
공식 코드SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction
https://github.com/servicenow/agentada
공식 코드AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
https://github.com/servicenow/browsergym
공식 코드WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
https://github.com/servicenow/promptmix-emnlp-2023
공식 코드PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
https://github.com/servicenow/workarena
공식 코드WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
https://github.com/seu-kse/SparqlSession
공식 코드https://github.com/seukgcode/fastkge
공식 코드Fast and Continual Knowledge Graph Embedding via Incremental LoRA
https://github.com/seungheondoh/lp-music-caps
공식 코드https://github.com/seungheondoh/lp-music-dialog
공식 코드Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language Models
https://github.com/seungheondoh/music-text-representation-pp
공식 코드Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music Retrieval
https://github.com/seungyoon1/llm-as-a-judge-human-eval
공식 코드Can Language Models Evaluate Human Written Text? Case Study on Korean Student Writing for Education
https://github.com/sevinjt/owl2vec4oa
공식 코드OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment
https://github.com/seyedparsa/let-me-think
공식 코드Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones
https://github.com/shafnehal/samcares
공식 코드https://github.com/shailaja183/objectconceptlearning
공식 코드Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.