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HellaSwag
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation
Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst
Elastic Weight Consolidation for Full-Parameter Continual Pre-Training of Gemma2
DataDecide: How to Predict Best Pretraining Data with Small Experiments
Domain-Adaptive Continued Pre-Training of Small Language Models
What the HellaSwag? On the Validity of Common-Sense Reasoning Benchmarks
More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment
Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models
HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
FinerWeb-10BT: Refining Web Data with LLM-Based Line-Level Filtering
SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
Towards Multilingual LLM Evaluation for European Languages
Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs
GRIN: GRadient-INformed MoE
You can remove GPT2's LayerNorm by fine-tuning
$\texttt{metabench}$ -- A Sparse Benchmark to Measure General Ability in Large Language Models
Promises, Outlooks and Challenges of Diffusion Language Modeling
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
SaGE: Evaluating Moral Consistency in Large Language Models
Attacks on Node Attributes in Graph Neural Networks
Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models
An Open Source Data Contamination Report for Large Language Models
Who's Harry Potter? Approximate Unlearning in LLMs
Contrastive Decoding Improves Reasoning in Large Language Models
In-Contextual Gender Bias Suppression for Large Language Models
Toward Adversarial Training on Contextualized Language Representation
Training Compute-Optimal Large Language Models
When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Comparing Test Sets with Item Response Theory
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too
Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning
HellaSwag: Can a Machine Really Finish Your Sentence?
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