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MMLU

Papers

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Showing 201–250 of 340 papers

TitleStatusHype
FRAMES: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy—0
Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training—0
QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-TuningCode0
Evaluation of Large Language Models via Coupled Token GenerationCode0
Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?—0
LLM-Powered Benchmark Factory: Reliable, Generic, and EfficientCode0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding—0
HardML: A Benchmark For Evaluating Data Science And Machine Learning knowledge and reasoning in AI—0
Humanity's Last Exam—0
On the Reasoning Capacity of AI Models and How to Quantify It—0
Is your LLM trapped in a Mental Set? Investigative study on how mental sets affect the reasoning capabilities of LLMs—0
Explain-Query-Test: Self-Evaluating LLMs Via Explanation and Comprehension DiscrepancyCode0
DNA 1.0 Technical Report—0
Inference-Time-Compute: More Faithful? A Research Note—0
CHAIR -- Classifier of Hallucination as ImproverCode0
Unraveling Indirect In-Context Learning Using Influence Functions—0
Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs—0
Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluation—0
SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity—0
ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case StudyCode0
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design—0
ChainRank-DPO: Chain Rank Direct Preference Optimization for LLM Rankers—0
Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs—0
Nanoscaling Floating-Point (NxFP): NanoMantissa, Adaptive Microexponents, and Code Recycling for Direct-Cast Compression of Large Language Models—0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering—0
Llama 3 Meets MoE: Efficient UpcyclingCode0
Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation—0
Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset—0
Noise Injection Reveals Hidden Capabilities of Sandbagging Language ModelsCode0
The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance?—0
Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents—0
Simple and Provable Scaling Laws for the Test-Time Compute of Large Language Models—0
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference—0
Predicting Emergent Capabilities by Finetuning—0
Learning from "Silly" Questions Improves Large Language Models, But Only Slightly—0
GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs—0
Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models—0
Reasoning Robustness of LLMs to Adversarial Typographical Errors—0
Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents—0
TODO: Enhancing LLM Alignment with Ternary PreferencesCode0
Project MPG: towards a generalized performance benchmark for LLM capabilities—0
Adaptive Dense Reward: Understanding the Gap Between Action and Reward Space in Alignment—0
Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning—0
BenTo: Benchmark Task Reduction with In-Context TransferabilityCode0
MIND: Math Informed syNthetic Dialogues for Pretraining LLMs—0
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks—0
Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context LearningCode0
Towards Multilingual LLM Evaluation for European Languages—0
Upcycling Large Language Models into Mixture of Experts—0
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All datasetsSIOP 2020/2021MMLU-ProVCTK

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1go ahead, make my dataFinal_score61.72—Unverified
2#GreedyCowFinal_score61.63—Unverified
3Don't Ask Us yFinal_score61.4—Unverified
4Data_and_ConfusedFinal_score60.96—Unverified
5WafflesFinal_score60.91—Unverified
6raakaFinal_score60.91—Unverified
7Team ProcrustinationFinal_score60.64—Unverified
8Axiom Consulting PartnersFinal_score60.63—Unverified
9Lets_Be_FairFinal_score60.23—Unverified
10goonersFinal_score60.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Orange-mini0-shot MRR99.19—Unverified
#ModelMetricClaimedVerifiedStatus
1HybridBeam+SI-SDRi13.3—Unverified