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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/moonshotai/kimina-prover-preview

공식 코드

Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning

https://github.com/moreno98/gradbias

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GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models

https://github.com/morganf33/gnr

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Generative News Recommendation

https://github.com/mosesananta/qless

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QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-Tuning

https://github.com/mozerwang/loong

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Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA

https://github.com/mpi-lab/bc-hoi

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Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection

https://github.com/mramg-bench/mramg

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MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation

https://github.com/mrgiovanni/radgpt

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RadGPT: Constructing 3D Image-Text Tumor Datasets

https://github.com/mrzilinxiao/autover

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Grounding Language Models for Visual Entity Recognition

https://github.com/mtkresearch/rad-bench

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RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues

https://github.com/muellerlab/synthesizing_interpretable_control_policies

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Synthesizing Interpretable Control Policies through Large Language Model Guided Search

https://github.com/muktac5/ciir-liverag

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CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training

https://github.com/multimodal-art-projection/map-neo

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MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series

https://github.com/mungomeng/survival-xsurv

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Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer

https://github.com/murongyue/llm_mot_cascade

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Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning

https://github.com/mushfiqur11/simpledc-dataset

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Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning

https://github.com/mustafakarabag/llmchameleon

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Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game

https://github.com/mxpoliakov/multi-meta-rag

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Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata

https://github.com/my-ai-university/finite-element-method

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AI-University: An LLM-based platform for instructional alignment to scientific classrooms

https://github.com/myazann/ap-bots

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Improving RAG for Personalization with Author Features and Contrastive Examples

https://github.com/myeon9h/planrag

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PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers

https://github.com/mysong7nlper/chatgpt_as_keyphrase_extractor

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Large Language Models as Zero-Shot Keyphrase Extractors: A Preliminary Empirical Study

https://github.com/n4feng/ResponseQualityAssessment

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Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality

https://github.com/nadavlab/CPLLM

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CPLLM: Clinical Prediction with Large Language Models

https://github.com/naist-nlp/CC_RAG

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Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain

https://github.com/namin/llm-verified-with-monte-carlo-tree-search

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VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search

https://github.com/nan-huo/micro-act

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Micro-Act: Mitigate Knowledge Conflict in Question Answering via Actionable Self-Reasoning

https://github.com/narabzad/genir-evaluation

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A Comparison of Methods for Evaluating Generative IR

https://github.com/narabzad/prompt-sensitivity-relevance-judgements

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A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance Judgment

https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness

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When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

https://github.com/naumannaeem/bea_2025

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NeuralNexus at BEA 2025 Shared Task: Retrieval-Augmented Prompting for Mistake Identification in AI Tutors

https://github.com/naver-ai/korean-safety-benchmarks

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KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application

https://github.com/naver-ai/tablevqabench

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TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

https://github.com/naver-intel-co-lab/gaudi-lavcap

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LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport

https://github.com/naver/bergen

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BERGEN: A Benchmarking Library for Retrieval-Augmented Generation

https://github.com/navid-aub/linkedin-dataset

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The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention

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