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Large Language Model

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

https://github.com/ict-bigdatalab/utility_judgments

공식 코드

Are Large Language Models Good at Utility Judgments?

https://github.com/ictmldm/SCAPE

공식 코드

Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation

https://github.com/ictnlp/LevelRAG

공식 코드

LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers

https://github.com/ictnlp/auto-rag

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Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models

https://github.com/ictnlp/bayling

공식 코드

BayLing 2: A Multilingual Large Language Model with Efficient Language Alignment

https://github.com/ictnlp/flexrag

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FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation

https://github.com/ictnlp/stream-omni

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Stream-Omni: Simultaneous Multimodal Interactions with Large Language-Vision-Speech Model

https://github.com/ictnlp/truthx

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TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space

https://github.com/ida-fbk/multimodalpattern

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A Pattern to Align Them All: Integrating Different Modalities to Define Multi-Modal Entities

https://github.com/idansc/mrr-ndcg

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Ensemble of MRR and NDCG models for Visual Dialog

https://github.com/idea-emdoor-lab/distilcodec

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UniTTS: An end-to-end TTS system without decoupling of acoustic and semantic information

https://github.com/idea-finai/ragllava

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MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training

https://github.com/idea-finai/ragvl

공식 코드

MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training

https://github.com/idea-finai/tog

공식 코드

Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

https://github.com/idea-finai/tog-2

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Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation

https://github.com/idea-research/rexseek

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Referring to Any Person

https://github.com/idekerlab/llm_evaluation_for_gene_set_interpretation

공식 코드

Evaluation of large language models for discovery of gene set function

https://github.com/idiap/class-imbalance-multilingual-ft

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Understanding the effects of language-specific class imbalance in multilingual fine-tuning

https://github.com/idllresearch/malicious-gpt

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Malla: Demystifying Real-world Large Language Model Integrated Malicious Services

https://github.com/idt-iti/vidctx

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VidCtx: Context-aware Video Question Answering with Image Models

https://github.com/ieit-yuan/yuan-2.0

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YUAN 2.0: A Large Language Model with Localized Filtering-based Attention

https://github.com/ielab/larmor

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Leveraging LLMs for Unsupervised Dense Retriever Ranking

https://github.com/iesl/narrative-driven-rec-mint

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Large Language Model Augmented Narrative Driven Recommendations

https://github.com/ighina/llmclimate2024

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Efficient Aspect-Based Summarization of Climate Change Reports with Small Language Models

https://github.com/ignorejjj/longrefiner

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Hierarchical Document Refinement for Long-context Retrieval-augmented Generation

https://github.com/iieir-km/ComplEx-NNE_AER

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Improving Knowledge Graph Embedding Using Simple Constraints

https://github.com/iieir-km/RUGE

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Knowledge Graph Embedding with Iterative Guidance from Soft Rules

https://github.com/iiot-tbb/dialogue_dde

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PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model

https://github.com/iiscleap/care

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Leveraging Content and Acoustic Representations for Speech Emotion Recognition

https://github.com/ilampard/lamp

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Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning

https://github.com/illuin-tech/grouse

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GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering

https://github.com/ilyatyagin/dyport

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Dyport: Dynamic Importance-based Hypothesis Generation Benchmarking Technique

https://github.com/imamnurby/transducer-tuning

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Transducer Tuning: Efficient Model Adaptation for Software Tasks Using Code Property Graphs

https://github.com/imeanai/webcanvas

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WebCanvas: Benchmarking Web Agents in Online Environments

https://github.com/imenjaoua/hybrid-code-review

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Combining Large Language Models with Static Analyzers for Code Review Generation

https://github.com/imethanlee/knowphish

공식 코드

KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection

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