Can Large Language Models Understand Context?
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2402.00858
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Published
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20
reasoning
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Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 15 -
ReFT: Reasoning with Reinforced Fine-Tuning
Paper • 2401.08967 • Published • 26 -
The Impact of Reasoning Step Length on Large Language Models
Paper • 2401.04925 • Published • 15 -
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
Paper • 2401.04398 • Published • 18 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 102 -
Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
Paper • 2311.04892 • Published • 1 -
More Agents Is All You Need
Paper • 2402.05120 • Published • 46 -
Grandmaster-Level Chess Without Search
Paper • 2402.04494 • Published • 62 -
The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models
Paper • 2401.05618 • Published • 1 -
Divide-or-Conquer? Which Part Should You Distill Your LLM?
Paper • 2402.15000 • Published • 22 -
System 2 Attention (is something you might need too)
Paper • 2311.11829 • Published • 38 -
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Paper • 2403.09629 • Published • 54 -
On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial
Paper • 2403.14380 • Published • 1 -
Orca-Math: Unlocking the potential of SLMs in Grade School Math
Paper • 2402.14830 • Published • 23 -
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Paper • 2404.02575 • Published • 46 -
Compression Represents Intelligence Linearly
Paper • 2404.09937 • Published • 27