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Showing 801–850 of 1596 papers

TitleStatusHype
Step-level Value Preference Optimization for Mathematical ReasoningCode3
CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer—0
MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM FinetuningCode3
Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference FeedbackCode7
Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language ModelsCode2
Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language ModelsCode3
ReMI: A Dataset for Reasoning with Multiple Images—0
Collective Constitutional AI: Aligning a Language Model with Public InputCode1
Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8BCode5
Can I understand what I create? Self-Knowledge Evaluation of Large Language Models—0
Human Learning about AI—0
CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuningCode2
A multi-core periphery perspective: Ranking via relative centrality—0
Lean Workbook: A large-scale Lean problem set formalized from natural language math problemsCode4
DICE: Detecting In-distribution Contamination in LLM's Fine-tuning Phase for Math ReasoningCode1
Improve Mathematical Reasoning in Language Models by Automated Process Supervision—0
NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language ModelsCode0
mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language ModelsCode0
D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models—0
Code Pretraining Improves Entity Tracking Abilities of Language Models—0
Cutting Through the Noise: Boosting LLM Performance on Math Word ProblemsCode0
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation—0
TAIA: Large Language Models are Out-of-Distribution Data LearnersCode1
MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn InteractionsCode1
Yuan 2.0-M32: Mixture of Experts with Attention RouterCode2
Arithmetic Reasoning with LLM: Prolog Generation & Permutation—0
LoRA-XS: Low-Rank Adaptation with Extremely Small Number of ParametersCode2
Autoformalizing Euclidean GeometryCode2
MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time—0
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision ModelsCode2
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs—0
Large Language Models Can Self-Correct with Key Condition Verification—0
Can LLMs Solve longer Math Word Problems Better?Code0
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis ModelsCode1
"Turing Tests" For An AI Scientist—0
Investigating Symbolic Capabilities of Large Language Models—0
MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics BenchmarkCode2
Multiple-Choice Questions are Efficient and Robust LLM EvaluatorsCode1
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving—0
DOP: Diagnostic-Oriented Prompting for Large Language Models in Mathematical CorrectionCode0
Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings—0
A safety realignment framework via subspace-oriented model fusion for large language modelsCode0
Meaning-Typed Programming: Language Abstraction and Runtime for Model-Integrated Applications—0
TANQ: An open domain dataset of table answered questionsCode1
MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical ReasoningCode3
MathDivide: Improved mathematical reasoning by large language models—0
Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions?Code0
Learning to Solve Geometry Problems via Simulating Human Dual-Reasoning ProcessCode0
Aligning Tutor Discourse Supporting Rigorous Thinking with Tutee Content Mastery for Predicting Math Achievement—0
LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought—0
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