Large Language Model
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rsummers11/LEAVS
공식 코드https://github.com/ruc-gsai/yulan-chat
공식 코드https://github.com/ruc-gsai/yulan-ir
공식 코드RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
https://github.com/ruc-gsai/yulan-rec
공식 코드User Behavior Simulation with Large Language Model based Agents
https://github.com/ruc-nlpir/flashrag
공식 코드FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
https://github.com/ruc-nlpir/omnieval
공식 코드OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain
https://github.com/rucaibox/allo
공식 코드Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model Alignment
https://github.com/rucaibox/lsvcr
공식 코드A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation
https://github.com/rucaibox/r1-searcher-plus
공식 코드R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning
https://github.com/rucaibox/rear
공식 코드REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
https://github.com/rucaibox/simpledeepsearcher
공식 코드SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
https://github.com/rucaibox/structgpt
공식 코드StructGPT: A General Framework for Large Language Model to Reason over Structured Data
https://github.com/rucaibox/ufin
공식 코드UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
https://github.com/rui9812/knowtrace
공식 코드KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing
https://github.com/ruikangliu/IntactKV
공식 코드IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact
https://github.com/ruizhong961230/llmo
공식 코드Large Language Model Assisted Adversarial Robustness Neural Architecture Search
https://github.com/ruizhong961230/zso
공식 코드Leveraging Large Language Model to Generate a Novel Metaheuristic Algorithm with CRISPE Framework
https://github.com/run-llama/llama_index
공식 코드https://github.com/rupakdas18/SemEval-2017-Task-4-A-B-C-using-BERT
공식 코드SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT
https://github.com/rupertluo/valley
공식 코드Valley: Video Assistant with Large Language model Enhanced abilitY
https://github.com/rutgerswiselab/genrec
공식 코드https://github.com/rymarinelli/leaking_lora
공식 코드Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models
https://github.com/ryuichi-sumida/lufy
공식 코드https://github.com/ryuryukke/HowYouPromptMatters
공식 코드https://github.com/ryuryukke/OUTFOX
공식 코드https://github.com/ryutaro-a/sumrec
공식 코드SumRec: A Framework for Recommendation using Open-Domain Dialogue
https://github.com/s-nlp/s3
공식 코드S3: A Simple Strong Sample-effective Multimodal Dialog System
https://github.com/s-vigneshwaran/revisiting-the-classics
공식 코드https://github.com/s222416822/llmqfl
공식 코드LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
https://github.com/sa4s-serc/llm4adr
공식 코드https://github.com/sai990323/tallrec
공식 코드https://github.com/saifkhichi96/situational-llm
공식 코드SituationalLLM: Proactive language models with scene awareness for dynamic, contextual task guidance
https://github.com/sail-sg/agent-smith
공식 코드Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
https://github.com/sail-sg/regmix
공식 코드RegMix: Data Mixture as Regression for Language Model Pre-training
https://github.com/sakanaai/edinet-bench
공식 코드EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.