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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/idea-finai/ragllava
공식 코드https://github.com/idea-finai/ragvl
공식 코드https://github.com/idea-finai/tog
공식 코드Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
https://github.com/idea-finai/tog-2
공식 코드https://github.com/idea-research/rexseek
공식 코드https://github.com/idekerlab/llm_evaluation_for_gene_set_interpretation
공식 코드Evaluation of large language models for discovery of gene set function
https://github.com/idiap/class-imbalance-multilingual-ft
공식 코드Understanding the effects of language-specific class imbalance in multilingual fine-tuning
https://github.com/idirlab/kgcompletion
공식 코드Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study
https://github.com/idllresearch/malicious-gpt
공식 코드Malla: Demystifying Real-world Large Language Model Integrated Malicious Services
https://github.com/idt-iti/vidctx
공식 코드VidCtx: Context-aware Video Question Answering with Image Models
https://github.com/ieit-yuan/yuan-2.0
공식 코드YUAN 2.0: A Large Language Model with Localized Filtering-based Attention
https://github.com/ielab/larmor
공식 코드https://github.com/iesl/narrative-driven-rec-mint
공식 코드Large Language Model Augmented Narrative Driven Recommendations
https://github.com/ighina/llmclimate2024
공식 코드Efficient Aspect-Based Summarization of Climate Change Reports with Small Language Models
https://github.com/ignorejjj/longrefiner
공식 코드Hierarchical Document Refinement for Long-context Retrieval-augmented Generation
https://github.com/iieir-km/ComplEx-NNE_AER
공식 코드Improving Knowledge Graph Embedding Using Simple Constraints
https://github.com/iieir-km/RUGE
공식 코드Knowledge Graph Embedding with Iterative Guidance from Soft Rules
https://github.com/iiot-tbb/dialogue_dde
공식 코드PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model
https://github.com/iiscleap/care
공식 코드Leveraging Content and Acoustic Representations for Speech Emotion Recognition
https://github.com/ilampard/lamp
공식 코드Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
https://github.com/illuin-tech/grouse
공식 코드GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering
https://github.com/ilyatyagin/dyport
공식 코드Dyport: Dynamic Importance-based Hypothesis Generation Benchmarking Technique
https://github.com/imamnurby/transducer-tuning
공식 코드Transducer Tuning: Efficient Model Adaptation for Software Tasks Using Code Property Graphs
https://github.com/imeanai/webcanvas
공식 코드https://github.com/imenjaoua/hybrid-code-review
공식 코드Combining Large Language Models with Static Analyzers for Code Review Generation
https://github.com/imethanlee/knowphish
공식 코드https://github.com/immortals88/demo-sea
공식 코드https://github.com/imnearth/coat
공식 코드https://github.com/imran2205/logicrag
공식 코드https://github.com/ina-foss/is24_news_topic
공식 코드https://github.com/index-tts/index-tts
공식 코드IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System
https://github.com/infini-ai-lab/ape
공식 코드APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding
https://github.com/informagi/embert
공식 코드https://github.com/informalsystems/fuzzmo_use_case2
공식 코드Accessible Smart Contracts Verification: Synthesizing Formal Models with Tamed LLMs
https://github.com/ink-usc/predicting-big-bench
공식 코드How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.