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Papers

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Showing 401–439 of 439 papers

TitleStatusHype
AlignedCoT: Prompting Large Language Models via Native-Speaking DemonstrationsCode0
First-Step Advantage: Importance of Starting Right in Multi-Step Math Reasoning—0
SAIE Framework: Support Alone Isn't Enough -- Advancing LLM Training with Adversarial Remarks—0
The ART of LLM Refinement: Ask, Refine, and Trust—0
Let's Reinforce Step by Step—0
Prompt Engineering a Prompt Engineer—0
The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback—0
SEGO: Sequential Subgoal Optimization for Mathematical Problem-SolvingCode0
Let's reward step by step: Step-Level reward model as the Navigators for Reasoning—0
DavIR: Data Selection via Implicit Reward for Large Language Models—0
KwaiYiiMath: Technical Report—0
From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference—0
Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward Reasoning in Math Word ProblemsCode0
Think before you speak: Training Language Models With Pause Tokens—0
Large Language Models as Analogical Reasoners—0
Adapting LLM Agents with Universal Feedback in Communication—0
UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities—0
Contrastive Decoding Improves Reasoning in Large Language Models—0
EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context LearningCode0
Exploring an LM to generate Prolog Predicates from Mathematics Questions—0
MathAttack: Attacking Large Language Models Towards Math Solving Ability—0
No Train Still Gain. Unleash Mathematical Reasoning of Large Language Models with Monte Carlo Tree Search Guided by Energy Function—0
Exploring Equation as a Better Intermediate Meaning Representation for Numerical ReasoningCode0
A mixed policy to improve performance of language models on math problemsCode0
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models—0
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning—0
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic SystemsCode0
PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuningCode0
RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought—0
Hint of Thought prompting: an explainable and zero-shot approach to reasoning tasks with LLMs—0
Self-Evaluation Guided Beam Search for Reasoning—0
Teaching Small Language Models to Reason—0
Distilling Reasoning Capabilities into Smaller Language ModelsCode0
Explicit Knowledge Transfer for Weakly-Supervised Code Generation—0
Solving math word problems with process- and outcome-based feedback—0
Large Language Models Can Self-Improve—0
Transcending Scaling Laws with 0.1% Extra Compute—0
Complexity-Based Prompting for Multi-Step Reasoning—0
Making Large Language Models Better Reasoners with Step-Aware Verifier—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1XolverAccuracy98.1—Unverified
2Orange-mini0-shot MRR98—Unverified