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Showing 1101–1150 of 1596 papers

TitleStatusHype
Scaling up ridge regression for brain encoding in a massive individual fMRI datasetCode0
Large Language Models Are Struggle to Cope with Unreasonability in Math Problems—0
ML2SC: Deploying Machine Learning Models as Smart Contracts on the Blockchain—0
Few-Shot Recalibration of Language Models—0
The Invalsi Benchmarks: measuring Linguistic and Mathematical understanding of Large Language Models in Italian—0
Automate Knowledge Concept Tagging on Math Questions with LLMs—0
To Err is Machine: Vulnerability Detection Challenges LLM Reasoning—0
From Large to Tiny: Distilling and Refining Mathematical Expertise for Math Word Problems with Weakly Supervision—0
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science—0
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?—0
PARAMANU-AYN: Pretrain from scratch or Continual Pretraining of LLMs for Legal Domain Adaptation?—0
Instructing Large Language Models to Identify and Ignore Irrelevant ConditionsCode0
An upper bound of the mutation probability in the genetic algorithm for general 0-1 knapsack problem—0
What Makes Math Word Problems Challenging for LLMs?Code0
Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement LearningCode0
Sabiá-2: A New Generation of Portuguese Large Language Models—0
Hydrodynamics of Markets:Hidden Links Between Physics and Finance—0
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks—0
Self-Consistency Boosts Calibration for Math Reasoning—0
Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models—0
SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small ModelsCode0
FineMath: A Fine-Grained Mathematical Evaluation Benchmark for Chinese Large Language Models—0
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM—0
Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word ProblemCode0
Evaluating and Optimizing Educational Content with Large Language Model JudgmentsCode0
MathScale: Scaling Instruction Tuning for Mathematical ReasoningCode0
Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning—0
The Claude 3 Model Family: Opus, Sonnet, Haiku—0
Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training—0
Experimenting with Generative AI: Does ChatGPT Really Increase Everyone's Productivity?—0
ClickTree: A Tree-based Method for Predicting Math Students' Performance Based on Clickstream Data—0
PRSA: Prompt Stealing Attacks against Real-World Prompt Services—0
Data Interpreter: An LLM Agent For Data Science—0
Adversarial Math Word Problem GenerationCode0
MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical ReasoningCode0
MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs—0
How Do Humans Write Code? Large Models Do It the Same Way TooCode0
Brain-Inspired Two-Stage Approach: Enhancing Mathematical Reasoning by Imitating Human Thought ProcessesCode0
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models—0
LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks—0
Orca-Math: Unlocking the potential of SLMs in Grade School Math—0
Mathematical Opportunities in Digital Twins (MATH-DT)—0
Language Models with Conformal Factuality Guarantees—0
AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and GuardrailsCode0
Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications—0
GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements—0
EvoGPT-f: An Evolutionary GPT Framework for Benchmarking Formal Math Languages—0
Understanding the Progression of Educational Topics via Semantic Matching—0
V-STaR: Training Verifiers for Self-Taught Reasoners—0
In-Context Principle Learning from MistakesCode0
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