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Benchmarking

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코드

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

https://github.com/zhou-zoey/rmb-reward-model-benchmark

공식 코드

RMB: Comprehensively Benchmarking Reward Models in LLM Alignment

https://github.com/zhourobotics/llm-mrs-survey

공식 코드

Large Language Models for Multi-Robot Systems: A Survey

https://github.com/zhouweixiao/KGDS

공식 코드

What are they talking about? Benchmarking Large Language Models for Knowledge-Grounded Discussion Summarization

https://github.com/zhouxian/FluidLab

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FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation

https://github.com/zhouzhengqd/beard

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation

https://github.com/zhudelong/elevator_button_recognition

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A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition

https://github.com/zhxlia/multitat

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MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering

https://github.com/zhymlumine/nbbench

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NbBench: Benchmarking Language Models for Comprehensive Nanobody Tasks

https://github.com/ziegler-ingo/cleavage_prediction

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What cleaves? Is proteasomal cleavage prediction reaching a ceiling?

https://github.com/zihan-liu-00/dl_for_peptide

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Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding

https://github.com/zihanwangki/x-tc

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A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches

https://github.com/zikeyuan/gracore

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GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models

https://github.com/zip-group/litv2

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Fast Vision Transformers with HiLo Attention

https://github.com/ziplab/litv2

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Fast Vision Transformers with HiLo Attention

https://github.com/zirak-ai/pashtoocr

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PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-resource Pashto Language

https://github.com/zivenzhu/multi-chart-qa

공식 코드

MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems

https://github.com/zjq0455/PTQ-Bench

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Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis

https://github.com/zjukg/kg-llm-papers

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Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?

https://github.com/zjunlp/easyedit

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CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs

https://github.com/zjunlp/factchd

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FactCHD: Benchmarking Fact-Conflicting Hallucination Detection

https://github.com/zjunlp/worfbench

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Benchmarking Agentic Workflow Generation

https://github.com/zk2172-columbia/constellation-dataset

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Constellation Dataset: Benchmarking High-Altitude Object Detection for an Urban Intersection

https://github.com/zli12321/qa_metrics

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PEDANTS: Cheap but Effective and Interpretable Answer Equivalence

https://github.com/zlwang-cs/OfficeBench

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OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automation

https://github.com/zo-bench/zo-llm

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Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark

https://github.com/zoeparman/benchmark-general-imaging

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Evaluating Feature Attribution Methods in the Image Domain

https://github.com/zpengmei/xxmd

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Beyond MD17: the reactive xxMD dataset

https://github.com/zpf0117b/CLMB

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CLMB: deep contrastive learning for robust metagenomic binning

https://github.com/zqiao11/tscil

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Class-incremental Learning for Time Series: Benchmark and Evaluation

https://github.com/zrqiao/NeuralPLexer

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State-specific protein-ligand complex structure prediction with a multi-scale deep generative model

https://github.com/zurichnlp/bless

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BLESS: Benchmarking Large Language Models on Sentence Simplification

https://github.com/zxuandang/atd-via-flows

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Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows

https://github.com/zzy-zhou/dsec-mos

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Event-Free Moving Object Segmentation from Moving Ego Vehicle

https://gitlab.com/micronano_public/tb-mlff

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Evaluating the Transferability of Machine-Learned Force Fields for Material Property Modeling

https://gitlab.com/mit-mers/safe-visual-mapf

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Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning

https://gitlab.com/pierregtch/motor_embedding_benchmark

공식 코드

Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets

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