Large Language Model
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sysll/CovidLLM
공식 코드https://github.com/sysuselab/rustevo
공식 코드RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code Generation
https://github.com/szeighami/nudge
공식 코드NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval
https://github.com/t-sciq/t-sciq
공식 코드https://github.com/taco-group/drama-x
공식 코드DRAMA-X: A Fine-grained Intent Prediction and Risk Reasoning Benchmark For Driving
https://github.com/tae2hee/coqah
공식 코드https://github.com/taegyeong-lee/makd
공식 코드Multi-aspect Knowledge Distillation with Large Language Model
https://github.com/taeheej/4bit-quantization-in-vector-embedding-for-rag
공식 코드https://github.com/taeheej/lightweight-relevance-grader-in-rag
공식 코드https://github.com/tag-research/tag-bench
공식 코드https://github.com/taichengguo/llm_multiagents_survey_papers
공식 코드Large Language Model based Multi-Agents: A Survey of Progress and Challenges
https://github.com/tamsiuhin/kracl
공식 코드KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
https://github.com/tan92hl/complex-question-answering-evaluation-of-chatgpt
공식 코드https://github.com/tan92hl/complex-question-answering-evaluation-of-gpt-family
공식 코드https://github.com/tanay2001/core
공식 코드CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation
https://github.com/tanchongmin/arc-challenge
공식 코드https://github.com/tanghulu6/llama3-8b-mob
공식 코드Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction
https://github.com/tangqiaoyu/selfretrieval
공식 코드Self-Retrieval: End-to-End Information Retrieval with One Large Language Model
https://github.com/tanny411/trutheval
공식 코드TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability
https://github.com/tanqiujiang/rapid
공식 코드RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models
https://github.com/tanwimallick/chatvis
공식 코드ChatVis: Automating Scientific Visualization with a Large Language Model
https://github.com/tanyuqian/knowledge-harvest-from-lms
공식 코드BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models
https://github.com/taokz/FedR
공식 코드https://github.com/taowu974/leam
공식 코드LEAM: A Prompt-only Large Language Model-enabled Antenna Modeling Method
https://github.com/tapilot-crossing/tapilot_code
공식 코드Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents
https://github.com/tatsu-lab/alpaca_farm
공식 코드Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment
https://github.com/taugroup/thinktank
공식 코드https://github.com/tayebiarasteh/radiorag
공식 코드https://github.com/taylorwwebb/emergent_analogies_llm
공식 코드https://github.com/tbenst/silent_speech
공식 코드A Cross-Modal Approach to Silent Speech with LLM-Enhanced Recognition
https://github.com/tclresearcheurope/edgewisepersona
공식 코드EdgeWisePersona: A Dataset for On-Device User Profiling from Natural Language Interactions
https://github.com/tdross/dna-llm
공식 코드Chaining thoughts and LLMs to learn DNA structural biophysics
https://github.com/teachable-ai-lab/tutor_gym
공식 코드TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students
https://github.com/teamnlp/typed-rag
공식 코드Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid Question Answering
https://github.com/teddy-xionggz/medrag
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.