SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought | Paper Citation Record

Observation dde7aa3c-ed48-48dc-9334-dde0d9709418 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Chain-of-thought prompting elicits reasoning in large language models

Reference 1

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Observation b5544222-9afa-4a04-8d7a-509298b1f0b5 · outbound

This paper cites Automatic Chain of Thought Prompting in Large Language Models.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Automatic Chain of Thought Prompting in Large Language Models

Reference 2

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This paper cites Active Prompting with Chain-of-Thought for Large Language Models.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Active Prompting with Chain-of-Thought for Large Language Models

Reference 3

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This paper cites Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Reference 4

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This paper cites On the inductive bias of stacking towards improving reasoning.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought On the inductive bias of stacking towards improving reasoning

Reference 5

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Observation 313c5f69-0ace-448c-9ea0-494ca7f5c280 · outbound

This paper cites Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement

Reference 6

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Observation 9f4f3ba8-e72d-40ff-9ee7-72bce75c6385 · outbound

This paper cites Reasoning with Latent Thoughts: On the Power of Looped Transformers.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Reasoning with Latent Thoughts: On the Power of Looped Transformers

Reference 7

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Observation 630c0bab-e66f-40da-9c36-a46d52065b34 · outbound

This paper cites Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA

Reference 8

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Observation 10cbbbc7-e76b-49f5-ae66-853b43d1d359 · outbound

This paper cites Decomposed Prompting: A Modular Approach for Solving Complex Tasks.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Decomposed Prompting: A Modular Approach for Solving Complex Tasks

Reference 9

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 10

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Observation 506b41a4-bf4b-49d9-9a15-72af3a406299 · outbound

This paper cites MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

Reference 11

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Observation 0d73ee2a-9e30-4d1c-adc5-174283bca839 · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 12

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Observation 389e2b64-ab36-4053-a795-36153396c59b · outbound

This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 13

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Observation 1ca5d673-53dc-49c1-84be-7ffa980db0f6 · outbound

This paper cites Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 14

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Observation 7e35f750-3716-42a7-88b1-4dbb4526b9d0 · outbound

This paper cites Teaching Large Language Models to Reason with Reinforcement Learning.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Teaching Large Language Models to Reason with Reinforcement Learning

Reference 15

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This paper cites Towards revealing the mystery behind chain of thought: a theoretical perspective.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Towards revealing the mystery behind chain of thought: a theoretical perspective

Reference 16

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This paper cites Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango

Reference 17

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Observation 412d19c6-9ed3-41f9-994b-9684f7cccb7f · outbound

This paper cites Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Reference 18

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Observation baa17150-f9e6-4f3c-a93c-994f0a5da3ad · outbound

This paper cites Implicit Chain of Thought Reasoning via Knowledge Distillation.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Implicit Chain of Thought Reasoning via Knowledge Distillation

Reference 19

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Observation 8eef7af6-2169-4f1a-9e79-346184e6a43f · outbound

This paper cites Training Large Language Models to Reason in a Continuous Latent Space.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Training Large Language Models to Reason in a Continuous Latent Space

Reference 20

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Observation ef4547e2-1f80-4b00-8d1d-3a9278426291 · outbound

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Universal Transformers

Reference 21

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Observation f426f003-757e-443c-991e-ed36f67f5254 · outbound

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Looped transformers as programmable computers

Reference 22

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This paper cites Looped Transformers for Length Generalization.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Looped Transformers for Length Generalization

Reference 23

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Sparse Universal Transformer

Reference 24

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This paper cites SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

Reference 25

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This paper cites Distilling the Knowledge in a Neural Network.

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Distilling the Knowledge in a Neural Network

Reference 26

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Knowledge distillation: A survey

Reference 27

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Distilling knowledge via knowledge review

Reference 28

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Sequence-level knowledge distillation

Reference 29

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 30

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Knowledge Distillation of Black-Box Large Language Models

Reference 31

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty

Reference 32

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Less is more: Task-aware layer-wise distillation for language model compression

Reference 33

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought On the efficacy of knowledge distillation

Reference 34

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Improved knowledge distillation via teacher assistant

Reference 35

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Distillation Scaling Laws

Reference 36

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Qwen2.5: A party of foundation models, September 2024

Reference 37

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

Reference 38

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Qwen2 Technical Report

Reference 39

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Llamafactory: Unified efficient fine-tuning of 100+ language models

Reference 40

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Instruction Tuning with GPT-4

Reference 41

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Alpaca-cot: An instruction fine-tuning platform with instruction data collection and unified large language models interface

Reference 42

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought WikiQA: A challenge dataset for open-domain question answering

Reference 43

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Code alpaca: An instruction-following llama model for code generation

Reference 44

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models

Reference 45

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought The language model evaluation harness, 07 2024

Reference 46

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 47

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 48

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought TruthfulQA: Measuring how models mimic human falsehoods

Reference 49

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Training verifiers to solve math word problems, 2021

Reference 50

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Measuring massive multitask language understanding

Reference 51

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Coqa: A conversational question answering challenge

Reference 52

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 53

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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Program Synthesis with Large Language Models

Reference 54

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