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Collections including paper arxiv:2308.13259

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Paper • 2204.07705 • Published Apr 16, 2022 • 1
Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering

Paper • 2308.13259 • Published Aug 25, 2023 • 2
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

Paper • 2309.05653 • Published Sep 11, 2023 • 10
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Paper • 2309.12284 • Published Sep 21, 2023 • 19

Ada-Instruct: Adapting Instruction Generators for Complex Reasoning

Paper • 2310.04484 • Published Oct 6, 2023 • 5
Diversity of Thought Improves Reasoning Abilities of Large Language Models

Paper • 2310.07088 • Published Oct 11, 2023 • 5
Adapting Large Language Models via Reading Comprehension

Paper • 2309.09530 • Published Sep 18, 2023 • 77
Democratizing Reasoning Ability: Tailored Learning from Large Language Model

Paper • 2310.13332 • Published Oct 20, 2023 • 14

Branch-Solve-Merge Improves Large Language Model Evaluation and Generation

Paper • 2310.15123 • Published Oct 23, 2023 • 7
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models

Paper • 2310.13671 • Published Oct 20, 2023 • 18
Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection

Paper • 2310.05035 • Published Oct 8, 2023 • 1
Chain-of-Thought Reasoning is a Policy Improvement Operator

Paper • 2309.08589 • Published Sep 15, 2023 • 1

KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval

Paper • 2310.15511 • Published Oct 24, 2023 • 4
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search

Paper • 2310.13227 • Published Oct 20, 2023 • 13
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning

Paper • 2310.04474 • Published Oct 6, 2023 • 2
AgentTuning: Enabling Generalized Agent Abilities for LLMs

Paper • 2310.12823 • Published Oct 19, 2023 • 35

Dissecting In-Context Learning of Translations in GPTs

Paper • 2310.15987 • Published Oct 24, 2023 • 5
In-Context Learning Creates Task Vectors

Paper • 2310.15916 • Published Oct 24, 2023 • 42
ZeroGen: Efficient Zero-shot Learning via Dataset Generation

Paper • 2202.07922 • Published Feb 16, 2022 • 1
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques

Paper • 2310.08101 • Published Oct 12, 2023 • 2

WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia

Paper • 2305.14292 • Published May 23, 2023 • 1
Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps

Paper • 2312.07796 • Published Dec 12, 2023
RAGAS: Automated Evaluation of Retrieval Augmented Generation

Paper • 2309.15217 • Published Sep 26, 2023 • 3
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering

Paper • 2210.02627 • Published Oct 6, 2022

LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Paper • 2309.12307 • Published Sep 21, 2023 • 88
Chain-of-Verification Reduces Hallucination in Large Language Models

Paper • 2309.11495 • Published Sep 20, 2023 • 37
Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering

Paper • 2308.13259 • Published Aug 25, 2023 • 2
Cognitive Mirage: A Review of Hallucinations in Large Language Models

Paper • 2309.06794 • Published Sep 13, 2023 • 1

Iterated Decomposition: Improving Science Q&A by Supervising Reasoning Processes

Paper • 2301.01751 • Published Jan 4, 2023
Question Decomposition Improves the Faithfulness of Model-Generated Reasoning

Paper • 2307.11768 • Published Jul 17, 2023 • 12
Contrastive Decoding Improves Reasoning in Large Language Models

Paper • 2309.09117 • Published Sep 17, 2023 • 37
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding

Paper • 2307.15337 • Published Jul 28, 2023 • 36

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