Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper
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2211.04325
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Published
AI Engineering
A collection of arXiv papers from Chip Huyen's AI Engineering organized by chapter and ordered by when each appears in the book.
Note Start of Introduction to Building AI Applications with Foundation Models
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 17 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 1 -
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Paper • 2303.10130 • Published • 4 -
CoEdIT: Text Editing by Task-Specific Instruction Tuning
Paper • 2305.09857 • Published • 7 -
Generative Agents: Interactive Simulacra of Human Behavior
Paper • 2304.03442 • Published • 12 -
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Paper • 2305.14233 • Published • 6
Measuring Massive Multitask Language Understanding
Paper • 2009.03300 • Published • 3Note End of Introduction to Building AI Applications with Foundation Models
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Paper • 1910.10683 • Published • 11Note Start of Understanding Foundation Models
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Textbooks Are All You Need
Paper • 2306.11644 • Published • 142 -
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning
Paper • 2304.05613 • Published • 1 -
Attention Is All You Need
Paper • 1706.03762 • Published • 55 -
Sequence to Sequence Learning with Neural Networks
Paper • 1409.3215 • Published • 3 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 244 -
Deep Learning using Rectified Linear Units (ReLU)
Paper • 1803.08375 • Published -
Gaussian Error Linear Units (GELUs)
Paper • 1606.08415 • Published -
The Llama 3 Herd of Models
Paper • 2407.21783 • Published • 114 -
Generative Adversarial Networks
Paper • 1406.2661 • Published • 4 -
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
Paper • 2110.13985 • Published -
Efficiently Modeling Long Sequences with Structured State Spaces
Paper • 2111.00396 • Published • 3 -
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
Paper • 2212.14052 • Published • 1 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 108 -
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Paper • 1701.06538 • Published • 6 -
Flamingo: a Visual Language Model for Few-Shot Learning
Paper • 2204.14198 • Published • 15 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 14 -
PaLM: Scaling Language Modeling with Pathways
Paper • 2204.02311 • Published • 2 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 13 -
Discovering Language Model Behaviors with Model-Written Evaluations
Paper • 2212.09251 • Published • 1 -
Inverse Scaling: When Bigger Isn't Better
Paper • 2306.09479 • Published • 9 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 10 -
Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
Paper • 2401.00448 • Published • 29 -
Emergent Abilities of Large Language Models
Paper • 2206.07682 • Published • 3 -
Model Dementia: Generated Data Makes Models Forget
Paper • 2305.17493 • Published • 4 -
Consent in Crisis: The Rapid Decline of the AI Data Commons
Paper • 2407.14933 • Published • 12 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 53 -
OpenAssistant Conversations -- Democratizing Large Language Model Alignment
Paper • 2304.07327 • Published • 6 -
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Paper • 2112.11446 • Published • 1 -
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Paper • 2403.04132 • Published • 38 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 17 -
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 57 -
Training Verifiers to Solve Math Word Problems
Paper • 2110.14168 • Published • 4 -
Detecting Hallucinated Content in Conditional Neural Sequence Generation
Paper • 2011.02593 • Published -
Shaking the foundations: delusions in sequence models for interaction and control
Paper • 2110.10819 • Published
How Language Model Hallucinations Can Snowball
Paper • 2305.13534 • Published • 3Note End of Understanding Foundation Models
Sharp seasonal threshold property for cooperative population dynamics with concave nonlinearities
Paper • 1804.07641 • PublishedNote Start of Evaluation Methodology
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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
Paper • 1905.00537 • Published • 2 -
Cross-Task Generalization via Natural Language Crowdsourcing Instructions
Paper • 2104.08773 • Published -
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Paper • 2406.01574 • Published • 46 -
A Survey on Evaluation of Large Language Models
Paper • 2307.03109 • Published • 42 -
Re-evaluating Evaluation
Paper • 1806.02643 • Published -
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Paper • 1709.00103 • Published • 1 -
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Paper • 2206.11249 • Published -
Evaluating Large Language Models Trained on Code
Paper • 2107.03374 • Published • 8 -
MTEB: Massive Text Embedding Benchmark
Paper • 2210.07316 • Published • 6 -
Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
Paper • 1607.07326 • Published -
Learning Transferable Visual Models From Natural Language Supervision
Paper • 2103.00020 • Published • 11 -
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
Paper • 2212.05171 • Published -
ImageBind: One Embedding Space To Bind Them All
Paper • 2305.05665 • Published • 5 -
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Paper • 2306.05685 • Published • 33 -
Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
Paper • 2404.04475 • Published -
Style Over Substance: Evaluation Biases for Large Language Models
Paper • 2307.03025 • Published -
How Do Data Science Workers Communicate Intermediate Results?
Paper • 2210.03305 • Published -
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Paper • 2305.11738 • Published • 8 -
Can Large Language Models Really Improve by Self-critiquing Their Own Plans?
Paper • 2310.08118 • Published • 1 -
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer
Paper • 2311.06720 • Published • 9 -
BLEURT: Learning Robust Metrics for Text Generation
Paper • 2004.04696 • Published -
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
Paper • 2310.08491 • Published • 54 -
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
Paper • 2306.05087 • Published • 6 -
JudgeLM: Fine-tuned Large Language Models are Scalable Judges
Paper • 2310.17631 • Published • 34 -
A General Language Assistant as a Laboratory for Alignment
Paper • 2112.00861 • Published • 2
Elo Uncovered: Robustness and Best Practices in Language Model Evaluation
Paper • 2311.17295 • PublishedNote End of Evaluation Methodology
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Paper • 2304.06364 • Published • 2Note Start of Evaluate AI Systems
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When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
Paper • 2402.01781 • Published • 2 -
Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
Paper • 2203.05227 • Published -
ChatGPT as a Factual Inconsistency Evaluator for Text Summarization
Paper • 2303.15621 • Published -
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Paper • 2303.08896 • Published • 4 -
Long-form factuality in large language models
Paper • 2403.18802 • Published • 25 -
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
Paper • 2312.06674 • Published • 8 -
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
Paper • 2305.08283 • Published -
The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation
Paper • 2301.01768 • Published -
From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection
Paper • 2102.12162 • Published -
Instruction-Following Evaluation for Large Language Models
Paper • 2311.07911 • Published • 20 -
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
Paper • 2309.11998 • Published • 25 -
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
Paper • 2310.00746 • Published • 1 -
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation
Paper • 2401.01275 • Published • 1 -
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Paper • 1803.05457 • Published • 2 -
HellaSwag: Can a Machine Really Finish Your Sentence?
Paper • 1905.07830 • Published • 4 -
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Paper • 2109.07958 • Published • 1 -
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
Paper • 1907.10641 • Published • 1 -
Measuring Mathematical Problem Solving With the MATH Dataset
Paper • 2103.03874 • Published • 5 -
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Paper • 2009.13081 • Published -
The NarrativeQA Reading Comprehension Challenge
Paper • 1712.07040 • Published -
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
Paper • 1809.02789 • Published -
GPQA: A Graduate-Level Google-Proof Q&A Benchmark
Paper • 2311.12022 • Published • 31 -
MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning
Paper • 2310.16049 • Published • 4 -
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Paper • 2206.04615 • Published • 5 -
How is ChatGPT's behavior changing over time?
Paper • 2307.09009 • Published • 24 -
Holistic Evaluation of Language Models
Paper • 2211.09110 • Published
Pretraining on the Test Set Is All You Need
Paper • 2309.08632 • Published • 2Note End of Evaluate AI Systems
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
Paper • 2404.13208 • Published • 39Note Start of Prompt Engineering
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Lost in the Middle: How Language Models Use Long Contexts
Paper • 2307.03172 • Published • 40 -
RULER: What's the Real Context Size of Your Long-Context Language Models?
Paper • 2404.06654 • Published • 35 -
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Paper • 2201.11903 • Published • 11 -
OpenPrompt: An Open-source Framework for Prompt-learning
Paper • 2111.01998 • Published • 1 -
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
Paper • 2310.03714 • Published • 34 -
Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
Paper • 2309.16797 • Published -
TextGrad: Automatic "Differentiation" via Text
Paper • 2406.07496 • Published • 31 -
Universal and Transferable Adversarial Attacks on Aligned Language Models
Paper • 2307.15043 • Published • 2 -
Jailbreaking Black Box Large Language Models in Twenty Queries
Paper • 2310.08419 • Published -
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
Paper • 2302.12173 • Published -
How do Hyenas deal with Human Speech? Speech Recognition and Translation with ConfHyena
Paper • 2402.13208 • Published -
Gmail Smart Compose: Real-Time Assisted Writing
Paper • 1906.00080 • Published -
Language Models as Knowledge Bases?
Paper • 1909.01066 • Published -
Extracting Training Data from Large Language Models
Paper • 2012.07805 • Published • 1 -
Are Large Pre-Trained Language Models Leaking Your Personal Information?
Paper • 2205.12628 • Published -
Scalable Extraction of Training Data from (Production) Language Models
Paper • 2311.17035 • Published • 3 -
Extracting Training Data from Diffusion Models
Paper • 2301.13188 • Published • 2
PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Paper • 2306.04528 • Published • 3Note End of Prompt Engineering
Reading Wikipedia to Answer Open-Domain Questions
Paper • 1704.00051 • PublishedNote Start of RAG and Agents
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper • 2005.11401 • Published • 10 -
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Paper • 2104.08663 • Published • 3 -
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
Paper • 2405.15793 • Published • 5 -
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
Paper • 2304.09842 • Published • 1 -
Reasoning with Language Model is Planning with World Model
Paper • 2305.14992 • Published • 3 -
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 22 -
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Paper • 1809.09600 • Published • 3 -
Reflexion: Language Agents with Verbal Reinforcement Learning
Paper • 2303.11366 • Published • 5 -
Toolformer: Language Models Can Teach Themselves to Use Tools
Paper • 2302.04761 • Published • 11 -
Gorilla: Large Language Model Connected with Massive APIs
Paper • 2305.15334 • Published • 5 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 9 -
Keep Me Updated! Memory Management in Long-term Conversations
Paper • 2210.08750 • Published
Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
Paper • 2311.08719 • PublishedNote End of RAG and Agents
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Paper • 1611.04558 • PublishedNote Start of Finetuning
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Code Llama: Open Foundation Models for Code
Paper • 2308.12950 • Published • 25 -
Scaling Instruction-Finetuned Language Models
Paper • 2210.11416 • Published • 7 -
BloombergGPT: A Large Language Model for Finance
Paper • 2303.17564 • Published • 22 -
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? An Examination on Several Typical Tasks
Paper • 2305.05862 • Published • 4 -
Reducing Activation Recomputation in Large Transformer Models
Paper • 2205.05198 • Published -
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Paper • 2208.07339 • Published • 4 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 50 -
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Paper • 1511.00363 • Published -
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Paper • 1603.05279 • Published -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 97 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 610 -
On Stochastic Shell Models of Turbulence
Paper • 1712.05887 • Published -
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
Paper • 2305.17888 • Published • 1 -
Parameter-Efficient Transfer Learning for NLP
Paper • 1902.00751 • Published • 1 -
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Paper • 1804.07461 • Published • 4 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 35 -
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
Paper • 2106.10199 • Published -
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
Paper • 2309.12307 • Published • 88 -
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Paper • 2101.00190 • Published • 6 -
GPT Understands, Too
Paper • 2103.10385 • Published • 9 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 10 -
Measuring the Intrinsic Dimension of Objective Landscapes
Paper • 1804.08838 • Published -
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
Paper • 2012.13255 • Published • 3 -
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Paper • 1602.07360 • Published • 1 -
Stack More Layers Differently: High-Rank Training Through Low-Rank Updates
Paper • 2307.05695 • Published • 23 -
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 185 -
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
Paper • 2305.08252 • Published -
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Paper • 2401.17868 • Published -
ConvLoRA and AdaBN based Domain Adaptation via Self-Training
Paper • 2402.04964 • Published -
A Note on LoRA
Paper • 2404.05086 • Published -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 44 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
Accurate LoRA-Finetuning Quantization of LLMs via Information Retention
Paper • 2402.05445 • Published -
Overcoming catastrophic forgetting in neural networks
Paper • 1612.00796 • Published • 1 -
Communication-Efficient Learning of Deep Networks from Decentralized Data
Paper • 1602.05629 • Published -
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Paper • 2203.05482 • Published • 6 -
Editing Models with Task Arithmetic
Paper • 2212.04089 • Published • 6 -
Model Fusion via Optimal Transport
Paper • 1910.05653 • Published • 1 -
Git Re-Basin: Merging Models modulo Permutation Symmetries
Paper • 2209.04836 • Published • 1 -
Merging by Matching Models in Task Subspaces
Paper • 2312.04339 • Published • 2 -
Resolving Interference When Merging Models
Paper • 2306.01708 • Published • 14 -
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper • 2311.03099 • Published • 29 -
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints
Paper • 2212.05055 • Published • 5 -
Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 57
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Paper • 2312.15166 • Published • 58Note End of Finetuning
DataComp-LM: In search of the next generation of training sets for language models
Paper • 2406.11794 • Published • 50Note Start of Dataset Engineering
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Yi: Open Foundation Models by 01.AI
Paper • 2403.04652 • Published • 62 -
LIMA: Less Is More for Alignment
Paper • 2305.11206 • Published • 23 -
Nemotron-4 340B Technical Report
Paper • 2406.11704 • Published -
The Data Addition Dilemma
Paper • 2408.04154 • Published -
Orthogonal Fold & Cut
Paper • 2202.01293 • Published -
TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
Paper • 2305.11171 • Published • 2 -
One pixel attack for fooling deep neural networks
Paper • 1710.08864 • Published -
Maxout Networks
Paper • 1302.4389 • Published -
DeepFool: a simple and accurate method to fool deep neural networks
Paper • 1511.04599 • Published -
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Paper • 1903.12261 • Published -
CARLA: An Open Urban Driving Simulator
Paper • 1711.03938 • Published -
StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models
Paper • 2403.07714 • Published • 1 -
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Paper • 2309.12284 • Published • 18 -
Self-Instruct: Aligning Language Model with Self Generated Instructions
Paper • 2212.10560 • Published • 9 -
LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction
Paper • 2304.08460 • Published • 3 -
Self-Alignment with Instruction Backtranslation
Paper • 2308.06259 • Published • 42 -
TeGit: Generating High-Quality Instruction-Tuning Data with Text-Grounded Task Design
Paper • 2309.05447 • Published • 1 -
The False Promise of Imitating Proprietary LLMs
Paper • 2305.15717 • Published • 5 -
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Paper • 2404.01413 • Published -
On the Stability of Iterative Retraining of Generative Models on their own Data
Paper • 2310.00429 • Published -
A Tale of Tails: Model Collapse as a Change of Scaling Laws
Paper • 2402.07043 • Published • 15 -
Common 7B Language Models Already Possess Strong Math Capabilities
Paper • 2403.04706 • Published • 18 -
Distilling the Knowledge in a Neural Network
Paper • 1503.02531 • Published -
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper • 1910.01108 • Published • 14 -
Instruction Tuning with GPT-4
Paper • 2304.03277 • Published -
Deduplicating Training Data Makes Language Models Better
Paper • 2107.06499 • Published • 4 -
D4: Improving LLM Pretraining via Document De-Duplication and Diversification
Paper • 2308.12284 • Published -
Scaling Laws and Interpretability of Learning from Repeated Data
Paper • 2205.10487 • Published • 1
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
Paper • 2311.14212 • PublishedNote End of Dataset Engineering
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Paper • 1802.04799 • PublishedNote Start of Inference Optimization
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Rethinking the Value of Network Pruning
Paper • 1810.05270 • Published -
To prune, or not to prune: exploring the efficacy of pruning for model compression
Paper • 1710.01878 • Published • 1 -
Blockwise Parallel Decoding for Deep Autoregressive Models
Paper • 1811.03115 • Published • 2 -
Accelerating Large Language Model Decoding with Speculative Sampling
Paper • 2302.01318 • Published • 2 -
Fast Inference from Transformers via Speculative Decoding
Paper • 2211.17192 • Published • 5 -
Inference with Reference: Lossless Acceleration of Large Language Models
Paper • 2304.04487 • Published -
Revisiting fixed-point quantum search: proof of the quasi-Chebyshev lemma
Paper • 2403.02057 • Published -
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
Paper • 2401.10774 • Published • 55 -
Efficiently Scaling Transformer Inference
Paper • 2211.05102 • Published • 2 -
Longformer: The Long-Document Transformer
Paper • 2004.05150 • Published • 3 -
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention
Paper • 2405.12981 • Published • 32 -
Fast Transformer Decoding: One Write-Head is All You Need
Paper • 1911.02150 • Published • 6 -
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Paper • 2305.13245 • Published • 5 -
GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM
Paper • 2403.05527 • Published • 1 -
Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs
Paper • 2310.01801 • Published • 3 -
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
Paper • 2407.08608 • Published • 1 -
DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
Paper • 2401.09670 • Published • 1
Inference without Interference: Disaggregate LLM Inference for Mixed Downstream Workloads
Paper • 2401.11181 • PublishedNote End of Inference Optimization
Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences
Paper • 2404.12272 • Published • 1Note Start of AI Engineering Architecture and User Feedback
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From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
Paper • 1902.07742 • Published -
Using Natural Language for Reward Shaping in Reinforcement Learning
Paper • 1903.02020 • Published -
Inverse Reinforcement Learning with Natural Language Goals
Paper • 2008.06924 • Published -
Learning Rewards from Linguistic Feedback
Paper • 2009.14715 • Published -
Feedback-Based Self-Learning in Large-Scale Conversational AI Agents
Paper • 1911.02557 • Published -
A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems
Paper • 2010.12251 • Published -
Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback
Paper • 2208.03270 • Published -
System-Level Natural Language Feedback
Paper • 2306.13588 • Published • 10 -
Towards Understanding Sycophancy in Language Models
Paper • 2310.13548 • Published • 5