‘NN sampling’ tag
- See Also
- Gwern
-
Links
- “Do LLMs Estimate Uncertainty Well in Instruction-Following?”, Heo et al 2024
- “SimpleStrat: Diversifying Language Model Generation With Stratification”, Wong et al 2024
- “Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs”, Laine et al 2024
- “Be like a Goldfish, Don’t Memorize! Mitigating Memorization in Generative LLMs”, Hans et al 2024
- “Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass”, Shen et al 2024
- “Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo”, Zhao et al 2024
- “Σ-GPTs: A New Approach to Autoregressive Models”, Pannatier et al 2024
- “LLM Evaluators Recognize and Favor Their Own Generations”, Panickssery et al 2024
- “Re-Evaluating GPT-4’s Bar Exam Performance”, Martínez 2024
- “Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking”, Zelikman et al 2024
- “Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews”, Liang et al 2024
- “Chain-Of-Thought Reasoning Without Prompting”, Wang & Zhou 2024
- “The Non-Effect of Sampling Temperature on Problem Solving in GPT-3.5/GPT-4”, Renze & Guven 2024
- “Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM”, Lu et al 2024
- “GIVT: Generative Infinite-Vocabulary Transformers”, Tschannen et al 2023
- “Universal Self-Consistency for Large Language Model Generation”, Chen et al 2023
- “Controlled Text Generation via Language Model Arithmetic”, Dekoninck et al 2023
- “Language Model Inversion”, Morris et al 2023
- “SEDD: Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution”, Lou et al 2023
- “Let Models Speak Ciphers: Multiagent Debate through Embeddings”, Pham et al 2023
- “Contrastive Decoding Improves Reasoning in Large Language Models”, O’Brien & Lewis 2023
- “Accelerating LLM Inference With Staged Speculative Decoding”, Spector & Re 2023
- “Efficient Guided Generation for Large Language Models”, Willard & Louf 2023
- “Copy Is All You Need”, Lan et al 2023
- “Stay on Topic With Classifier-Free Guidance”, Sanchez et al 2023
- “Sequential Monte Carlo Steering of Large Language Models Using Probabilistic Programs”, Lew et al 2023
- “How Language Model Hallucinations Can Snowball”, Zhang et al 2023
- “Tractable Control for Autoregressive Language Generation”, Zhang et al 2023
- “MUX-PLMs: Pre-Training Language Models With Data Multiplexing”, Murahari et al 2023
- “Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models”, Aksitov et al 2023
- “DataMUX: Data Multiplexing for Neural Networks”, Murahari et al 2023
- “Witscript 3: A Hybrid AI System for Improvising Jokes in a Conversation”, Toplyn 2023
- “A Survey on Text Generation Using Generative Adversarial Networks”, Rosa & Papa 2022
- “Fast Inference from Transformers via Speculative Decoding”, Leviathan et al 2022
- “The CRINGE Loss: Learning What Language Not to Model”, Adolphs et al 2022
- “Contrastive Decoding: Open-Ended Text Generation As Optimization”, Li et al 2022
- “Help Me Write a Poem: Instruction Tuning As a Vehicle for Collaborative Poetry Writing (CoPoet)”, Chakrabarty et al 2022
- “Contrastive Search Is What You Need For Neural Text Generation”, Su & Collier 2022
- “Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models”, Vilnis et al 2022
- “Most Language Models Can Be Poets Too: An AI Writing Assistant and Constrained Text Generation Studio”, Roush et al 2022
- “Ask Me Anything (AMA): A Simple Strategy for Prompting Language Models”, Arora et al 2022
- “Out of One, Many: Using Language Models to Simulate Human Samples”, Argyle et al 2022
- “Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Ganguli et al 2022
- “Effidit: Your AI Writing Assistant”, Shi et al 2022
- “DIRECTOR: Generator-Classifiers For Supervised Language Modeling”, Arora et al 2022
- “RankGen: Improving Text Generation With Large Ranking Models”, Krishna et al 2022
- “Time Control: Language Modeling via Stochastic Processes”, Wang et al 2022
- “Controllable Natural Language Generation With Contrastive Prefixes”, Qian et al 2022
- “Using Natural Language Prompts for Machine Translation”, Garcia & Firat 2022
- “A Contrastive Framework for Neural Text Generation”, Su et al 2022
- “Typical Decoding for Natural Language Generation”, Meister et al 2022
- “FIGARO: Generating Symbolic Music With Fine-Grained Artistic Control”, Rütte et al 2022
- “A Survey of Controllable Text Generation Using Transformer-Based Pre-Trained Language Models”, Zhang et al 2022
- “FRUIT: Faithfully Reflecting Updated Information in Text”, IV et al 2021
- “NeuroLogic A✱esque Decoding: Constrained Text Generation With Lookahead Heuristics”, Lu et al 2021
- “Relating Neural Text Degeneration to Exposure Bias”, Chiang & Chen 2021
- “Program Synthesis With Large Language Models”, Austin et al 2021
- “Scarecrow: A Framework for Scrutinizing Machine Text”, Dou et al 2021
- “Time-Aware Language Models As Temporal Knowledge Bases”, Dhingra et al 2021
- “Machine Translation Decoding beyond Beam Search”, Leblond et al 2021
- “Controllable Generation from Pre-Trained Language Models via Inverse Prompting”, Zou et al 2021
- “Improving Diversity of Neural Text Generation via Inverse Probability Weighting”, Zhang et al 2021
- “There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It”, Wang et al 2021
- “A✱ Search Without Expansions: Learning Heuristic Functions With Deep Q-Networks”, Agostinelli et al 2021
- “MAUVE: Measuring the Gap Between Neural Text and Human Text Using Divergence Frontiers”, Pillutla et al 2021
- “Prefix-Tuning: Optimizing Continuous Prompts for Generation”, Li & Liang 2021
- “Bot-Adversarial Dialogue for Safe Conversational Agents”, Xu et al 2021
- “Collaborative Storytelling With Large-Scale Neural Language Models”, Nichols et al 2020
- “NeuroLogic Decoding: (Un)supervised Neural Text Generation With Predicate Logic Constraints”, Lu et al 2020
- “Interacting With GPT-2 to Generate Controlled and Believable Musical Sequences in ABC Notation”, Geerlings & Meroño-Peñuela 2020
- “Training Independent Subnetworks for Robust Prediction”, Havasi et al 2020
- “MEGATRON-CNTRL: Controllable Story Generation With External Knowledge Using Large-Scale Language Models”, Xu et al 2020
- “Weird AI Yankovic: Generating Parody Lyrics”, Riedl 2020
- “A Systematic Characterization of Sampling Algorithms for Open-Ended Language Generation”, Nadeem et al 2020
- “GeDi: Generative Discriminator Guided Sequence Generation”, Krause et al 2020
- “Mirostat: A Neural Text Decoding Algorithm That Directly Controls Perplexity”, Basu et al 2020
- “Progressive Generation of Long Text”, Tan et al 2020
- “This Word Does Not Exist”, Dimson 2020
- “True_poetry: Poetry Generator by GPT-2 With Meter and Rhyme Constraints”, Summers-Stay 2020
- “Blender: A State-Of-The-Art Open Source Chatbot”, Roller et al 2020
- “Trading Off Diversity and Quality in Natural Language Generation”, Zhang et al 2020
- “Rapformer: Conditional Rap Lyrics Generation With Denoising Autoencoders”, Nikolov et al 2020
- “A Hundred Visions and Revisions”, Binder 2020
- “Top-K Training of GANs: Improving GAN Performance by Throwing Away Bad Samples”, Sinha et al 2020
- “Towards a Human-Like Open-Domain Chatbot”, Adiwardana et al 2020
- “Controlling Text Generation With Plug and Play Language Models”, Liu et al 2019
- “Plug and Play Language Models: A Simple Approach to Controlled Text Generation”, Dathathri et al 2019
- “CTRL: A Conditional Transformer Language Model For Controllable Generation”, Keskar et al 2019
- “Neural Text Generation With Unlikelihood Training”, Welleck et al 2019
- “GROVER: Defending Against Neural Fake News”, Zellers et al 2019
- “The Curious Case of Neural Text Degeneration”, Holtzman et al 2019
- “Good News, Everyone! Context Driven Entity-Aware Captioning for News Images”, Biten et al 2019
- “GPT-2 Neural Network Poetry”, Gwern & Presser 2019
- “Insertion Transformer: Flexible Sequence Generation via Insertion Operations”, Stern et al 2019
- “Blockwise Parallel Decoding for Deep Autoregressive Models”, Stern et al 2018
- “Language GANs Falling Short”, Caccia et al 2018
- “Discriminator Rejection Sampling”, Azadi et al 2018
- “OCD: Optimal Completion Distillation for Sequence Learning”, Sabour et al 2018
- “Controlling Linguistic Style Aspects in Neural Language Generation”, Ficler & Goldberg 2017
- “Six Challenges for Neural Machine Translation”, Koehn & Knowles 2017
- “Language Generation With Recurrent Generative Adversarial Networks without Pre-Training”, Press et al 2017
- “A Deep Reinforced Model for Abstractive Summarization”, Paulus et al 2017
- “Learning to Generate Reviews and Discovering Sentiment”, Radford et al 2017
- “Improving Neural Machine Translation With Conditional Sequence Generative Adversarial Nets”, Yang et al 2017
- “Tuning Recurrent Neural Networks With Reinforcement Learning”, Jaques et al 2017
- “Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation”, Johnson et al 2016
- “WaveNet: A Generative Model for Raw Audio”, Oord et al 2016
- “Sequence Level Training With Recurrent Neural Networks”, Ranzato et al 2015
- “Generative Concatenative Nets Jointly Learn to Write and Classify Reviews”, Lipton et al 2015
- “Semi-Supervised Sequence Learning”, Dai & Le 2015
- “Scheduled Sampling for Sequence Prediction With Recurrent Neural Networks”, Bengio et al 2015
- “Controlling GPT-3 With Logit Bias”
- “Feature: Beam Search for Improving Global Quality of New Text Samples”
- “Exclude Top Choices (XTC): A Sampler That Boosts Creativity, Breaks Writing Clichés, and Inhibits Non-Verbatim Repetition”
- “Prompting Diverse Ideas: Increasing AI Idea Variance”
- “Pixels Still Beat Text: Attacking the OpenAI CLIP Model With Text Patches and Adversarial Pixel Perturbations”
- “Me, Myself, and AI: the Situational Awareness Dataset (SAD) for LLMs”
- “Apple or IPod? Easy Fix for Adversarial Textual Attacks on OpenAI's CLIP Model!”
- Sort By Magic
- Wikipedia
- Miscellaneous
- Bibliography
See Also
Gwern
“Research Ideas”, Gwern 2017
“GPT-3 Creative Fiction”, Gwern 2020
“Choose-Your-Own-Adventure AI Dungeon Games”, Gwern 2021
“RNN Metadata for Mimicking Author Style”, Gwern 2015
Links
“Do LLMs Estimate Uncertainty Well in Instruction-Following?”, Heo et al 2024
“SimpleStrat: Diversifying Language Model Generation With Stratification”, Wong et al 2024
SimpleStrat: Diversifying Language Model Generation with Stratification
“Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs”, Laine et al 2024
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
“Be like a Goldfish, Don’t Memorize! Mitigating Memorization in Generative LLMs”, Hans et al 2024
Be like a Goldfish, Don’t Memorize! Mitigating Memorization in Generative LLMs
“Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass”, Shen et al 2024
Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass
“Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo”, Zhao et al 2024
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
“Σ-GPTs: A New Approach to Autoregressive Models”, Pannatier et al 2024
“LLM Evaluators Recognize and Favor Their Own Generations”, Panickssery et al 2024
“Re-Evaluating GPT-4’s Bar Exam Performance”, Martínez 2024
“Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking”, Zelikman et al 2024
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
“Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews”, Liang et al 2024
“Chain-Of-Thought Reasoning Without Prompting”, Wang & Zhou 2024
“The Non-Effect of Sampling Temperature on Problem Solving in GPT-3.5/GPT-4”, Renze & Guven 2024
The Non-Effect of Sampling Temperature on Problem Solving in GPT-3.5/GPT-4
“Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM”, Lu et al 2024
Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
“GIVT: Generative Infinite-Vocabulary Transformers”, Tschannen et al 2023
“Universal Self-Consistency for Large Language Model Generation”, Chen et al 2023
Universal Self-Consistency for Large Language Model Generation
“Controlled Text Generation via Language Model Arithmetic”, Dekoninck et al 2023
“Language Model Inversion”, Morris et al 2023
“SEDD: Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution”, Lou et al 2023
SEDD: Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
“Let Models Speak Ciphers: Multiagent Debate through Embeddings”, Pham et al 2023
Let Models Speak Ciphers: Multiagent Debate through Embeddings
“Contrastive Decoding Improves Reasoning in Large Language Models”, O’Brien & Lewis 2023
Contrastive Decoding Improves Reasoning in Large Language Models
“Accelerating LLM Inference With Staged Speculative Decoding”, Spector & Re 2023
“Efficient Guided Generation for Large Language Models”, Willard & Louf 2023
“Copy Is All You Need”, Lan et al 2023
“Stay on Topic With Classifier-Free Guidance”, Sanchez et al 2023
“Sequential Monte Carlo Steering of Large Language Models Using Probabilistic Programs”, Lew et al 2023
Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs
“How Language Model Hallucinations Can Snowball”, Zhang et al 2023
“Tractable Control for Autoregressive Language Generation”, Zhang et al 2023
“MUX-PLMs: Pre-Training Language Models With Data Multiplexing”, Murahari et al 2023
MUX-PLMs: Pre-training Language Models with Data Multiplexing
“Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models”, Aksitov et al 2023
Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models
“DataMUX: Data Multiplexing for Neural Networks”, Murahari et al 2023
“Witscript 3: A Hybrid AI System for Improvising Jokes in a Conversation”, Toplyn 2023
Witscript 3: A Hybrid AI System for Improvising Jokes in a Conversation
“A Survey on Text Generation Using Generative Adversarial Networks”, Rosa & Papa 2022
A survey on text generation using generative adversarial networks
“Fast Inference from Transformers via Speculative Decoding”, Leviathan et al 2022
“The CRINGE Loss: Learning What Language Not to Model”, Adolphs et al 2022
“Contrastive Decoding: Open-Ended Text Generation As Optimization”, Li et al 2022
Contrastive Decoding: Open-ended Text Generation as Optimization
“Help Me Write a Poem: Instruction Tuning As a Vehicle for Collaborative Poetry Writing (CoPoet)”, Chakrabarty et al 2022
Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing (CoPoet)
“Contrastive Search Is What You Need For Neural Text Generation”, Su & Collier 2022
Contrastive Search Is What You Need For Neural Text Generation
“Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models”, Vilnis et al 2022
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
“Most Language Models Can Be Poets Too: An AI Writing Assistant and Constrained Text Generation Studio”, Roush et al 2022
“Ask Me Anything (AMA): A Simple Strategy for Prompting Language Models”, Arora et al 2022
Ask Me Anything (AMA): A simple strategy for prompting language models
“Out of One, Many: Using Language Models to Simulate Human Samples”, Argyle et al 2022
Out of One, Many: Using Language Models to Simulate Human Samples
“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Ganguli et al 2022
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
“Effidit: Your AI Writing Assistant”, Shi et al 2022
“DIRECTOR: Generator-Classifiers For Supervised Language Modeling”, Arora et al 2022
DIRECTOR: Generator-Classifiers For Supervised Language Modeling
“RankGen: Improving Text Generation With Large Ranking Models”, Krishna et al 2022
RankGen: Improving Text Generation with Large Ranking Models
“Time Control: Language Modeling via Stochastic Processes”, Wang et al 2022
“Controllable Natural Language Generation With Contrastive Prefixes”, Qian et al 2022
Controllable Natural Language Generation with Contrastive Prefixes
“Using Natural Language Prompts for Machine Translation”, Garcia & Firat 2022
“A Contrastive Framework for Neural Text Generation”, Su et al 2022
“Typical Decoding for Natural Language Generation”, Meister et al 2022
“FIGARO: Generating Symbolic Music With Fine-Grained Artistic Control”, Rütte et al 2022
FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control
“A Survey of Controllable Text Generation Using Transformer-Based Pre-Trained Language Models”, Zhang et al 2022
A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models
“FRUIT: Faithfully Reflecting Updated Information in Text”, IV et al 2021
“NeuroLogic A✱esque Decoding: Constrained Text Generation With Lookahead Heuristics”, Lu et al 2021
NeuroLogic A✱esque Decoding: Constrained Text Generation with Lookahead Heuristics
“Relating Neural Text Degeneration to Exposure Bias”, Chiang & Chen 2021
“Program Synthesis With Large Language Models”, Austin et al 2021
“Scarecrow: A Framework for Scrutinizing Machine Text”, Dou et al 2021
“Time-Aware Language Models As Temporal Knowledge Bases”, Dhingra et al 2021
“Machine Translation Decoding beyond Beam Search”, Leblond et al 2021
“Controllable Generation from Pre-Trained Language Models via Inverse Prompting”, Zou et al 2021
Controllable Generation from Pre-trained Language Models via Inverse Prompting
“Improving Diversity of Neural Text Generation via Inverse Probability Weighting”, Zhang et al 2021
Improving Diversity of Neural Text Generation via Inverse Probability Weighting
“There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It”, Wang et al 2021
There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It
“A✱ Search Without Expansions: Learning Heuristic Functions With Deep Q-Networks”, Agostinelli et al 2021
A✱ Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks
“MAUVE: Measuring the Gap Between Neural Text and Human Text Using Divergence Frontiers”, Pillutla et al 2021
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
“Prefix-Tuning: Optimizing Continuous Prompts for Generation”, Li & Liang 2021
“Bot-Adversarial Dialogue for Safe Conversational Agents”, Xu et al 2021
“Collaborative Storytelling With Large-Scale Neural Language Models”, Nichols et al 2020
Collaborative Storytelling with Large-scale Neural Language Models
“NeuroLogic Decoding: (Un)supervised Neural Text Generation With Predicate Logic Constraints”, Lu et al 2020
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints
“Interacting With GPT-2 to Generate Controlled and Believable Musical Sequences in ABC Notation”, Geerlings & Meroño-Peñuela 2020
Interacting with GPT-2 to Generate Controlled and Believable Musical Sequences in ABC Notation
“Training Independent Subnetworks for Robust Prediction”, Havasi et al 2020
“MEGATRON-CNTRL: Controllable Story Generation With External Knowledge Using Large-Scale Language Models”, Xu et al 2020
“Weird AI Yankovic: Generating Parody Lyrics”, Riedl 2020
“A Systematic Characterization of Sampling Algorithms for Open-Ended Language Generation”, Nadeem et al 2020
A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation
“GeDi: Generative Discriminator Guided Sequence Generation”, Krause et al 2020
“Mirostat: A Neural Text Decoding Algorithm That Directly Controls Perplexity”, Basu et al 2020
Mirostat: A Neural Text Decoding Algorithm that Directly Controls Perplexity
“Progressive Generation of Long Text”, Tan et al 2020
“This Word Does Not Exist”, Dimson 2020
“True_poetry: Poetry Generator by GPT-2 With Meter and Rhyme Constraints”, Summers-Stay 2020
true_poetry: Poetry generator by GPT-2 with meter and rhyme constraints
“Blender: A State-Of-The-Art Open Source Chatbot”, Roller et al 2020
“Trading Off Diversity and Quality in Natural Language Generation”, Zhang et al 2020
Trading Off Diversity and Quality in Natural Language Generation
“Rapformer: Conditional Rap Lyrics Generation With Denoising Autoencoders”, Nikolov et al 2020
Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders
“A Hundred Visions and Revisions”, Binder 2020
“Top-K Training of GANs: Improving GAN Performance by Throwing Away Bad Samples”, Sinha et al 2020
Top-K Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
“Towards a Human-Like Open-Domain Chatbot”, Adiwardana et al 2020
“Controlling Text Generation With Plug and Play Language Models”, Liu et al 2019
Controlling Text Generation with Plug and Play Language Models
“Plug and Play Language Models: A Simple Approach to Controlled Text Generation”, Dathathri et al 2019
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
“CTRL: A Conditional Transformer Language Model For Controllable Generation”, Keskar et al 2019
CTRL: A Conditional Transformer Language Model For Controllable Generation
“Neural Text Generation With Unlikelihood Training”, Welleck et al 2019
“GROVER: Defending Against Neural Fake News”, Zellers et al 2019
“The Curious Case of Neural Text Degeneration”, Holtzman et al 2019
“Good News, Everyone! Context Driven Entity-Aware Captioning for News Images”, Biten et al 2019
Good News, Everyone! Context driven entity-aware captioning for news images
“GPT-2 Neural Network Poetry”, Gwern & Presser 2019
“Insertion Transformer: Flexible Sequence Generation via Insertion Operations”, Stern et al 2019
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
“Blockwise Parallel Decoding for Deep Autoregressive Models”, Stern et al 2018
“Language GANs Falling Short”, Caccia et al 2018
“Discriminator Rejection Sampling”, Azadi et al 2018
“OCD: Optimal Completion Distillation for Sequence Learning”, Sabour et al 2018
“Controlling Linguistic Style Aspects in Neural Language Generation”, Ficler & Goldberg 2017
Controlling Linguistic Style Aspects in Neural Language Generation
“Six Challenges for Neural Machine Translation”, Koehn & Knowles 2017
“Language Generation With Recurrent Generative Adversarial Networks without Pre-Training”, Press et al 2017
Language Generation with Recurrent Generative Adversarial Networks without Pre-training
“A Deep Reinforced Model for Abstractive Summarization”, Paulus et al 2017
“Learning to Generate Reviews and Discovering Sentiment”, Radford et al 2017
“Improving Neural Machine Translation With Conditional Sequence Generative Adversarial Nets”, Yang et al 2017
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
“Tuning Recurrent Neural Networks With Reinforcement Learning”, Jaques et al 2017
Tuning Recurrent Neural Networks with Reinforcement Learning
“Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation”, Johnson et al 2016
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
“WaveNet: A Generative Model for Raw Audio”, Oord et al 2016
“Sequence Level Training With Recurrent Neural Networks”, Ranzato et al 2015
“Generative Concatenative Nets Jointly Learn to Write and Classify Reviews”, Lipton et al 2015
Generative Concatenative Nets Jointly Learn to Write and Classify Reviews
“Semi-Supervised Sequence Learning”, Dai & Le 2015
“Scheduled Sampling for Sequence Prediction With Recurrent Neural Networks”, Bengio et al 2015
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
“Controlling GPT-3 With Logit Bias”
“Feature: Beam Search for Improving Global Quality of New Text Samples”
Feature: beam search for improving global quality of new text samples
“Exclude Top Choices (XTC): A Sampler That Boosts Creativity, Breaks Writing Clichés, and Inhibits Non-Verbatim Repetition”
“Prompting Diverse Ideas: Increasing AI Idea Variance”
“Pixels Still Beat Text: Attacking the OpenAI CLIP Model With Text Patches and Adversarial Pixel Perturbations”
“Me, Myself, and AI: the Situational Awareness Dataset (SAD) for LLMs”
Me, Myself, and AI: the Situational Awareness Dataset (SAD) for LLMs:
“Apple or IPod? Easy Fix for Adversarial Textual Attacks on OpenAI's CLIP Model!”
Apple or iPod? Easy Fix for Adversarial Textual Attacks on OpenAI's CLIP Model!
Sort By Magic
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Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
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Wikipedia
Miscellaneous
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/doc/ai/nn/sampling/2022-arora-figure4-amapromptgenerationscalingvskshotwithmodelsize.jpg: -
/doc/ai/nn/sampling/2020-roller-facebook-blenderchatbot-ratedperformancevshumans.jpg: -
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https://homepages.inf.ed.ac.uk/abmayne/publications/sennrich2016NAACL.pdf -
https://mi.eng.cam.ac.uk/projects/cued-rnnlm/papers/Interspeech15.pdf -
https://openai.com/index/introducing-structured-outputs-in-the-api/#_5PYjnV1iAHOPKPupDztdZk -
https://workshop2015.iwslt.org/downloads/IWSLT_2015_RP_13.pdf:
Bibliography
-
https://arxiv.org/abs/2407.04694: “Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs”, -
https://arxiv.org/abs/2405.18400: “Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass”, -
https://arxiv.org/abs/2404.13076: “LLM Evaluators Recognize and Favor Their Own Generations”, -
https://link.springer.com/article/10.1007/s10506-024-09396-9: “Re-Evaluating GPT-4’s Bar Exam Performance”, -
https://arxiv.org/abs/2403.09629: “Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking”, -
https://arxiv.org/abs/2312.02116: “GIVT: Generative Infinite-Vocabulary Transformers”, -
https://arxiv.org/abs/2309.09117#facebook: “Contrastive Decoding Improves Reasoning in Large Language Models”, -
https://arxiv.org/abs/2306.17806#eleutherai: “Stay on Topic With Classifier-Free Guidance”, -
https://arxiv.org/abs/2306.03081: “Sequential Monte Carlo Steering of Large Language Models Using Probabilistic Programs”, -
https://arxiv.org/abs/2305.13534: “How Language Model Hallucinations Can Snowball”, -
https://arxiv.org/abs/2302.12441: “MUX-PLMs: Pre-Training Language Models With Data Multiplexing”, -
https://arxiv.org/abs/2212.11119: “A Survey on Text Generation Using Generative Adversarial Networks”, -
https://arxiv.org/abs/2210.15097: “Contrastive Decoding: Open-Ended Text Generation As Optimization”, -
https://arxiv.org/abs/2210.13669: “Help Me Write a Poem: Instruction Tuning As a Vehicle for Collaborative Poetry Writing (CoPoet)”, -
https://arxiv.org/abs/2210.14140: “Contrastive Search Is What You Need For Neural Text Generation”, -
https://arxiv.org/abs/2210.15458#google: “Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models”, -
https://aclanthology.org/2022.cai-1.2.pdf: “Most Language Models Can Be Poets Too: An AI Writing Assistant and Constrained Text Generation Studio”, -
https://arxiv.org/abs/2210.02441: “Ask Me Anything (AMA): A Simple Strategy for Prompting Language Models”, -
https://www.anthropic.com/red_teaming.pdf: “Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, -
https://arxiv.org/abs/2202.11822#google: “Using Natural Language Prompts for Machine Translation”, -
https://arxiv.org/abs/2107.01294#allen: “Scarecrow: A Framework for Scrutinizing Machine Text”, -
https://arxiv.org/abs/2101.00190: “Prefix-Tuning: Optimizing Continuous Prompts for Generation”, -
https://aclanthology.org/2021.naacl-main.235.pdf#facebook: “Bot-Adversarial Dialogue for Safe Conversational Agents”, -
https://www.thisworddoesnotexist.com/: “This Word Does Not Exist”, -
https://ai.meta.com/blog/state-of-the-art-open-source-chatbot/: “Blender: A State-Of-The-Art Open Source Chatbot”, -
https://arxiv.org/abs/2004.03965: “Rapformer: Conditional Rap Lyrics Generation With Denoising Autoencoders”, -
https://www.uber.com/blog/pplm/: “Controlling Text Generation With Plug and Play Language Models”, -
https://arxiv.org/abs/1909.05858#salesforce: “CTRL: A Conditional Transformer Language Model For Controllable Generation”, -
gpt-2: “GPT-2 Neural Network Poetry”, -
https://arxiv.org/abs/1811.02549: “Language GANs Falling Short”,