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Collections including paper arxiv:2311.06158
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Ada-Instruct: Adapting Instruction Generators for Complex Reasoning
Paper • 2310.04484 • Published • 5 -
Diversity of Thought Improves Reasoning Abilities of Large Language Models
Paper • 2310.07088 • Published • 5 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 81 -
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
Paper • 2310.13332 • Published • 16
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When can transformers reason with abstract symbols?
Paper • 2310.09753 • Published • 4 -
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 30 -
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
Paper • 2310.09520 • Published • 12 -
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 53
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Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2 -
Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering
Paper • 2308.13259 • Published • 2 -
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Paper • 2309.05653 • Published • 10 -
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Paper • 2309.12284 • Published • 18
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Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4 -
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Paper • 2310.13127 • Published • 12 -
Evaluating the Robustness to Instructions of Large Language Models
Paper • 2308.14306 • Published • 1
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ChipNeMo: Domain-Adapted LLMs for Chip Design
Paper • 2311.00176 • Published • 9 -
Language Models can be Logical Solvers
Paper • 2311.06158 • Published • 23 -
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Paper • 2311.05997 • Published • 37 -
Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs
Paper • 2311.05657 • Published • 32
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Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2 -
Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering
Paper • 2308.13259 • Published • 2 -
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Paper • 2309.05653 • Published • 10 -
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Paper • 2309.12284 • Published • 18
-
Ada-Instruct: Adapting Instruction Generators for Complex Reasoning
Paper • 2310.04484 • Published • 5 -
Diversity of Thought Improves Reasoning Abilities of Large Language Models
Paper • 2310.07088 • Published • 5 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 81 -
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
Paper • 2310.13332 • Published • 16
-
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4 -
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Paper • 2310.13127 • Published • 12 -
Evaluating the Robustness to Instructions of Large Language Models
Paper • 2308.14306 • Published • 1
-
When can transformers reason with abstract symbols?
Paper • 2310.09753 • Published • 4 -
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 30 -
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
Paper • 2310.09520 • Published • 12 -
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 53
-
ChipNeMo: Domain-Adapted LLMs for Chip Design
Paper • 2311.00176 • Published • 9 -
Language Models can be Logical Solvers
Paper • 2311.06158 • Published • 23 -
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Paper • 2311.05997 • Published • 37 -
Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs
Paper • 2311.05657 • Published • 32