Papers

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• West-of-N: Synthetic Preference Generation for Improved Reward Modeling

• Rishabh synthetic data talk

• Navigating the Geometry of Language: A New Approach to Synthetic Text Generation

• Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

• Generative AI for Synthetic Data Generation: Methods, Challenges and the Future

• Comprehensive Exploration of Synthetic Data Generation: A Survey

• Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning

• Generating Training Data with Language Models: Towards Zero-Shot Language Understanding

• ZEROGEN: Efficient Zero-shot Learning via Dataset Generation

• Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning

• ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback

• ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval

• Prototypical Verbalizer for Prompt-based Few-shot Tuning

• Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

• Mixture of Soft Prompts for Controllable Data Generation
  Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm

• Differentiable Quality Diversity

• Diversity of Thought Improves Reasoning Abilities of Large Language Models

• Instruction Diversity Drives Generalization To Unseen Tasks

• Diversity-Aware Ensembling of Language Models Based on Topological Data Analysis

• Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions
  Texygen: A Benchmarking Platform for Text Generation Models

• Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing

• Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

• Instruction Mining: High-Quality Instruction Data Selection for Large Language Models

• Co-training and Co-distillation for Quality Improvement and Compression of Language Models

• In-context Reinforcement Learning with Algorithm Distillation

• SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization

• Dataset Distillation: A Comprehensive Review

• Knowledge Distillation of Large Language Models

• GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models

• RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment

• IMPLICIT CHAIN OF THOUGHT REASONING VIA KNOWLEDGE DISTILLATION

• Latent Dataset Distillation with Diffusion Models

• Large Language Models Can Self-improve

• Self-Programming Artificial Intelligence Using Code-Generating Language Models

• Self-Instruct: Aligning Language Model with Self Generated Instructions

• Self Supervision Does Not Help Natural Language Supervision at Scale

• The Capacity for Moral Self-Correction in Large Language Models

• Self-planning Code Generation with Large Language Model

• Reflexion: an autonomous agent with dynamic memory and self-reflection

• Self-Refine: Iterative Refinement with Self-Feedback

• Teaching Large Language Models to Self-Debug

• Learning to Reason and Memorize with Self-Notes

• The Socratic Method for Self-Discovery in Large Language Models

• CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

• language model self improvement by reinforcement learning contemplation

• Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback

• GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction

• Generating Sequences by Learning to Self-Correct

• SelFee: Iterative Self-Revising LLM Empowered by Self-Feedback Generation

• Demystifying GPT Self-Repair for Code Generation

• Bring Your Own Data! Self-Supervised Evaluation for Large Language Models

• Long-range Language Modeling with Self-retrieval

• Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision

• Self-consistency for open-ended generations

• SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning

• Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies

• Shepherd: A Critic for Language Model Generation

• Self alignment with instruction back translation

• Reinforced Self-Training (ReST) for Language Modeling

• Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

• Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation

• SALMON: Self-Alignment with Principle-Following Reward Models

• Self-Specialization: Uncovering Latent Expertise within Large Language Models

• Large Language Models are Better Reasoners with Self-Verification

• SelfEval: Leveraging the discriminative nature of generative models for evaluation

• Language model self-teaching for domain adaptation

• Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift

• Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

• Self evaluation improves selective generation

• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

• MATH-SHEPHERD: VERIFY AND REINFORCE LLMS STEP-BY-STEP WITHOUT HUMAN ANNOTATIONS

• Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives

• Self-Rewarding Language Models

• ReFT: Reasoning with Reinforced Fine-Tuning

• Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution

• Self-Discover: Large Language Models Self-Compose Reasoning Structures

• CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay

• V-STaR: Training Verifiers for Self-Taught Reasoners

• Perils of Self-Feedback: Self-Bias Amplifies in Large Language Models

• Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

• Soft Self-Consistency Improves Language Model Agents

• the Dimension of Self-Directed Learning

• TOOLVERIFIER: Generalization to New Tools via Self-Verification

• Explorations of Self-Repair in Language Models

• Can Large Language Models Play Games? A Case Study of A Self-Play Approach

• Evolution through Large Models

• Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers

• EvoGPT-f: An Evolutionary GPT Framework for Benchmarking Formal Math Languages

• Large Language Models As Evolution Strategies

• Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap

• Quality-Diversity through AI Feedback

• ACES: GENERATING DIVERSE PROGRAMMING PUZZLES WITH AUTOTELIC LANGUAGE MODELS AND SEMANTIC DESCRIPTORS

• Language Model Crossover: Variation through Few-Shot Prompting

• EvoPrompting: Language Models for Code-Level Neural Architecture Search

• Quality Diversity through Human Feedback

• Large Language Models as Optimizers

• Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers

• Open-endedness via Modeling human Notions of Interestingness

• Human-Timescale Adaptation in an Open-Ended Task Space

• Weak-to-strong generalization

• Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts

• Open-Ended Generation of Diverse Adversarial Prompts

• POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer

• Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback

• Open-Ended Learning Leads to Generally Capable Agents

• Voyager: An Open-Ended Embodied Agent with Large Language Models

• Describe and Explain and Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents

• Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges

• WizardLM: Empowering Large Language Models to Follow Complex Instructions

• MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

• Comprehensive Exploration of Synthetic Data Generation: A Survey

• Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias

• Benchmarking and Improving Generator-Validator Consistency of Language Models

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• Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts