Elicit Machine Learning Reading List

Purpose

The purpose of this curriculum is to help new Elicit employees learn background in machine learning, with a focus on language models. I’ve tried to strike a balance between papers that are relevant for deploying ML in production and techniques that matter for longer-term scalability.

If you don’t work at Elicit yet - we’re hiring ML and software engineers.

How to read

Recommended reading order:

1. Read “Tier 1” for all topics
2. Read “Tier 2” for all topics
3. Etc

✨ = Added after 2025/11/26

Table of contents

• Fundamentals
  • Introduction to machine learning
  • Transformers
  • Key foundation model architectures
  • Training and finetuning
• Reasoning and runtime strategies
  • In-context reasoning
  • Task decomposition
  • Debate
  • Tool use and scaffolding
  • Honesty, factuality, and epistemics
• Applications
  • Science
  • Forecasting
  • Search and ranking
• ML in practice
  • Production deployment
  • Benchmarks
  • Datasets
• Advanced topics
  • World models and causality
  • Planning
  • Uncertainty, calibration, and active learning
  • Interpretability and model editing
  • Reinforcement learning
• The big picture
  • AI scaling
  • AI safety
  • Economic and social impacts
  • Philosophy
• Maintainer

Fundamentals

Introduction to machine learning

Tier 1

• A short introduction to machine learning
• But what is a neural network?
• Gradient descent, how neural networks learn

Tier 2

• An intuitive understanding of backpropagation
• What is backpropagation really doing?
• An introduction to deep reinforcement learning

Tier 3

• The spelled-out intro to neural networks and backpropagation: building micrograd (Karpathy)
• Backpropagation calculus

Transformers

Tier 1

• ✨ Intro to Large Language Models (Karpathy)
• But what is a GPT? Visual intro to transformers
• Attention in transformers, visually explained
• Attention? Attention!
• The Illustrated Transformer

Tier 2

• ✨ Deep Dive into LLMs like ChatGPT (Karpathy)
• Let's build the GPT Tokenizer (Karpathy)
• The Illustrated GPT-2 (Visualizing Transformer Language Models)
• Neural Machine Translation by Jointly Learning to Align and Translate
• Attention Is All You Need

Tier 3

• ✨ The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
• The Annotated Transformer
• TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
• Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
• A Mathematical Framework for Transformer Circuits

Tier 4+

• A Practical Survey on Faster and Lighter Transformers
• Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
• Memorizing Transformers
• Transformer Feed-Forward Layers Are Key-Value Memories

Key foundation model architectures

Tier 1

• Language Models are Unsupervised Multitask Learners (GPT-2)
• Language Models are Few-Shot Learners (GPT-3)

Tier 2

• ✨ DeepSeek-R1 (DeepSeek-R1)
• ✨ DeepSeek-V3 Technical Report (DeepSeek-V3)
• ✨ The Llama 3 Herd of Models (Llama 3)
• LLaMA: Open and Efficient Foundation Language Models (LLaMA)
• Training language models to follow instructions with human feedback (OpenAI Instruct)

Tier 3

• ✨ LLaMA 2: Open Foundation and Fine-Tuned Chat Models (LLaMA 2)
• ✨ Qwen2.5 Technical Report (Qwen2.5)
• ✨ Titans: Learning to Memorize at Test Time
• ✨ Byte Latent Transformer
• ✨ Phi-4 Technical Report (phi-4)

Tier 4+

• Evaluating Large Language Models Trained on Code (OpenAI Codex)
• Mistral 7B (Mistral)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5)
• Gemini: A Family of Highly Capable Multimodal Models (Gemini)
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Mamba)
• Scaling Instruction-Finetuned Language Models (Flan)
• Efficiently Modeling Long Sequences with Structured State Spaces (video) (S4)
• Consistency Models
• Model Card and Evaluations for Claude Models (Claude 2)
• OLMo: Accelerating the Science of Language Models
• PaLM 2 Technical Report (Palm 2)
• Textbooks Are All You Need II: phi-1.5 technical report (phi 1.5)
• Visual Instruction Tuning (LLaVA)
• A General Language Assistant as a Laboratory for Alignment
• Finetuned Language Models Are Zero-Shot Learners (Google Instruct)
• Galactica: A Large Language Model for Science
• LaMDA: Language Models for Dialog Applications (Google Dialog)
• OPT: Open Pre-trained Transformer Language Models (Meta GPT-3)
• PaLM: Scaling Language Modeling with Pathways (PaLM)
• Program Synthesis with Large Language Models (Google Codex)
• Scaling Language Models: Methods, Analysis & Insights from Training Gopher (Gopher)
• Solving Quantitative Reasoning Problems with Language Models (Minerva)
• UL2: Unifying Language Learning Paradigms (UL2)

Training and finetuning

Tier 2

• ✨ Deep Learning Tuning Playbook
• Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
• Learning to summarise with human feedback
• Training Verifiers to Solve Math Word Problems

Tier 3

• ✨ Better & Faster Large Language Models via Multi-token Prediction
• ✨ LoRA vs Full Fine-tuning: An Illusion of Equivalence
• ✨ QLoRA: Efficient Finetuning of Quantized LLMs
• Pretraining Language Models with Human Preferences
• Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Tier 4+

• Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
• Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
• Improving Code Generation by Training with Natural Language Feedback
• Language Modeling Is Compression
• LIMA: Less Is More for Alignment
• Learning to Compress Prompts with Gist Tokens
• Lost in the Middle: How Language Models Use Long Contexts
• LoRA: Low-Rank Adaptation of Large Language Models
• Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
• Reinforced Self-Training (ReST) for Language Modeling
• Solving olympiad geometry without human demonstrations
• Tell, don't show: Declarative facts influence how LLMs generalize
• Textbooks Are All You Need
• TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
• Training Language Models with Language Feedback at Scale
• Turing Complete Transformers: Two Transformers Are More Powerful Than One
• ByT5: Towards a token-free future with pre-trained byte-to-byte models
• Data Distributional Properties Drive Emergent In-Context Learning in Transformers
• Diffusion-LM Improves Controllable Text Generation
• ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
• Efficient Training of Language Models to Fill in the Middle
• ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
• Prefix-Tuning: Optimizing Continuous Prompts for Generation
• Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
• True Few-Shot Learning with Prompts -- A Real-World Perspective

Reasoning and runtime strategies

In-context reasoning

Tier 2

• ✨ Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
• Chain of Thought Prompting Elicits Reasoning in Large Language Models
• Large Language Models are Zero-Shot Reasoners (Let's think step by step)
• Self-Consistency Improves Chain of Thought Reasoning in Language Models

Tier 3

• ✨ s1: Simple test-time scaling
• ✨ Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs
• ✨ The Surprising Effectiveness of Test-Time Training for Abstract Reasoning
• ✨ Large Language Models Cannot Self-Correct Reasoning Yet
• Chain-of-Thought Reasoning Without Prompting

Tier 4+

• Why think step-by-step? Reasoning emerges from the locality of experience
• Baldur: Whole-Proof Generation and Repair with Large Language Models
• Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought
• Certified Reasoning with Language Models
• Hypothesis Search: Inductive Reasoning with Language Models
• LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations
• Stream of Search (SoS): Learning to Search in Language
• Training Chain-of-Thought via Latent-Variable Inference
• Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
• Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Task decomposition

Tier 1

• Supervise Process, not Outcomes
• Supervising strong learners by amplifying weak experts

Tier 2

• Tree of Thoughts: Deliberate Problem Solving with Large Language Models
• Factored cognition
• Iterated Distillation and Amplification
• Recursively Summarizing Books with Human Feedback
• Solving math word problems with process-based and outcome-based feedback

Tier 3

• Factored Verification: Detecting and Reducing Hallucination in Summaries of Academic Papers
• Faithful Reasoning Using Large Language Models
• Iterated Decomposition: Improving Science Q&A by Supervising Reasoning Processes
• Language Model Cascades

Tier 4+

• Decontextualization: Making Sentences Stand-Alone
• Factored Cognition Primer
• Graph of Thoughts: Solving Elaborate Problems with Large Language Models
• Parsel: A Unified Natural Language Framework for Algorithmic Reasoning
• AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts
• Challenging BIG-Bench tasks and whether chain-of-thought can solve them
• Evaluating Arguments One Step at a Time
• Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
• Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
• Measuring and narrowing the compositionality gap in language models
• PAL: Program-aided Language Models
• ReAct: Synergizing Reasoning and Acting in Language Models
• Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
• Show Your Work: Scratchpads for Intermediate Computation with Language Models
• Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
• Thinksum: probabilistic reasoning over sets using large language models

Debate

Tier 2

• AI safety via debate

Tier 3

• ✨ Avoiding Obfuscation with Prover-Estimator Debate
• ✨ Improving Factuality and Reasoning in Language Models through Multiagent Debate
• ✨ Prover-Verifier Games Improve Legibility of LLM Outputs
• Debate Helps Supervise Unreliable Experts

Tier 4+

• Scalable AI Safety via Doubly-Efficient Debate
• Two-Turn Debate Doesn’t Help Humans Answer Hard Reading Comprehension Questions

Tool use and scaffolding

Tier 2

• Measuring the impact of post-training enhancements
• WebGPT: Browser-assisted question-answering with human feedback

Tier 3

• ✨ Executable Code Actions Elicit Better LLM Agents
• ✨ GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
• ✨ TextGrad: Automatic "Differentiation" via Text
• AI capabilities can be significantly improved without expensive retraining
• Automated Statistical Model Discovery with Language Models

Tier 4+

• DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
• Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
• Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• ReGAL: Refactoring Programs to Discover Generalizable Abstractions

Honesty, factuality, and epistemics

Tier 2

• Self-critiquing models for assisting human evaluators

Tier 3

• What Evidence Do Language Models Find Convincing?
• How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions

Tier 4+

• Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
• Long-form factuality in large language models

Applications

Science

Tier 2

• ✨ AlphaEvolve: A coding agent for scientific and algorithmic discovery
• ✨ AlphaFold 3: Accurate structure prediction of biomolecular interactions

Tier 3

• ✨ Towards an AI Co-Scientist
• Can large language models provide useful feedback on research papers? A large-scale empirical analysis
• Large Language Models Encode Clinical Knowledge
• The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4
• A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Tier 4+

• Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
• Nougat: Neural Optical Understanding for Academic Documents
• Scim: Intelligent Skimming Support for Scientific Papers
• SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
• Towards Accurate Differential Diagnosis with Large Language Models
• Towards a Benchmark for Scientific Understanding in Humans and Machines
• A Search Engine for Discovery of Scientific Challenges and Directions
• A full systematic review was completed in 2 weeks using automation tools: a case study
• Fact or Fiction: Verifying Scientific Claims
• Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
• PEER: A Collaborative Language Model
• PubMedQA: A Dataset for Biomedical Research Question Answering
• SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts
• SciTail: A Textual Entailment Dataset from Science Question Answering

Forecasting

Tier 3

• ✨ Consistency Checks for Language Model Forecasters
• ✨ LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
• AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy
• Approaching Human-Level Forecasting with Language Models
• Forecasting Future World Events with Neural Networks

Tier 4+

• Are Transformers Effective for Time Series Forecasting?

Search and ranking

Tier 2

• Learning Dense Representations of Phrases at Scale
• Text and Code Embeddings by Contrastive Pre-Training (OpenAI embeddings)

Tier 3

• Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
• Not All Vector Databases Are Made Equal
• REALM: Retrieval-Augmented Language Model Pre-Training
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Task-aware Retrieval with Instructions

Tier 4+

• RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!
• Some Common Mistakes In IR Evaluation, And How They Can Be Avoided
• Boosting Search Engines with Interactive Agents
• ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
• Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
• UnifiedQA: Crossing Format Boundaries With a Single QA System

ML in practice

Production deployment

Tier 1

• Machine Learning in Python: Main developments and technology trends in data science, machine learning, and AI
• Machine Learning: The High Interest Credit Card of Technical Debt

Tier 2

• Designing Data-Intensive Applications
• A Recipe for Training Neural Networks (Karpathy)

Benchmarks

Tier 2

• ✨ GAIA: a benchmark for General AI Assistants
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
• TruthfulQA: Measuring How Models Mimic Human Falsehoods

Tier 3

• ✨ RE-Bench: Evaluating Frontier AI R&D Capabilities
• ✨ SimpleQA: Measuring Short-Form Factuality
• ✨ ARC Prize 2024: Technical Report
• ✨ FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI
• Measuring Massive Multitask Language Understanding

Tier 4+

• FLEX: Unifying Evaluation for Few-Shot NLP
• Holistic Evaluation of Language Models (HELM)
• True Few-Shot Learning with Language Models
• ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
• Measuring Mathematical Problem Solving With the MATH Dataset
• QuALITY: Question Answering with Long Input Texts, Yes!
• SCROLLS: Standardized CompaRison Over Long Language Sequences
• What Will it Take to Fix Benchmarking in Natural Language Understanding?

Datasets

Tier 2

• Common Crawl
• The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Tier 3

• ✨ FineWeb: Decanting the Web for the Finest Text Data at Scale
• Dialog Inpainting: Turning Documents into Dialogs
• MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
• Microsoft Academic Graph

Advanced topics

World models and causality

Tier 3

• Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
• Language Models Represent Space and Time

Tier 4+

• Amortizing intractable inference in large language models
• CLADDER: Assessing Causal Reasoning in Language Models
• Causal Bayesian Optimization
• Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
• Generative Agents: Interactive Simulacra of Human Behavior
• Passive learning of active causal strategies in agents and language models

Planning

Tier 4+

• Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
• Cognitive Architectures for Language Agents

Uncertainty, calibration, and active learning

Tier 2

• Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
• A Simple Baseline for Bayesian Uncertainty in Deep Learning
• Plex: Towards Reliability using Pretrained Large Model Extensions

Tier 3

• ✨ Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
• Active Preference Inference using Language Models and Probabilistic Reasoning
• Eliciting Human Preferences with Language Models
• Describing Differences between Text Distributions with Natural Language
• Teaching Models to Express Their Uncertainty in Words

Tier 4+

• Active Learning by Acquiring Contrastive Examples
• Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning
• STaR-GATE: Teaching Language Models to Ask Clarifying Questions
• Active Testing: Sample-Efficient Model Evaluation
• Uncertainty Estimation for Language Reward Models

Interpretability and model editing

Tier 2

• ✨ Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
• ✨ Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
• Discovering Latent Knowledge in Language Models Without Supervision

Tier 3

• ✨ Scaling and Evaluating Sparse Autoencoders
• ✨ Opening the AI black box: program synthesis via mechanistic interpretability
• Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
• Representation Engineering: A Top-Down Approach to AI Transparency
• Studying Large Language Model Generalization with Influence Functions

Tier 4+

• Codebook Features: Sparse and Discrete Interpretability for Neural Networks
• Eliciting Latent Predictions from Transformers with the Tuned Lens
• How do Language Models Bind Entities in Context?
• Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
• Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
• Uncovering mesa-optimization algorithms in Transformers
• Fast Model Editing at Scale
• Git Re-Basin: Merging Models modulo Permutation Symmetries
• Locating and Editing Factual Associations in GPT
• Mass-Editing Memory in a Transformer

Reinforcement learning

Tier 2

• ✨ DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models (GRPO)
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• Reflexion: Language Agents with Verbal Reinforcement Learning
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (AlphaZero)
• MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Tier 3

• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
• AlphaStar: mastering the real-time strategy game StarCraft II
• Decision Transformer
• Mastering Atari Games with Limited Data (EfficientZero)
• Mastering Stratego, the classic game of imperfect information (DeepNash)

Tier 4+

• AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
• Bayesian Reinforcement Learning with Limited Cognitive Load
• Contrastive Prefence Learning: Learning from Human Feedback without RL
• Grandmaster-Level Chess Without Search
• A data-driven approach for learning to control computers
• Acquisition of Chess Knowledge in AlphaZero
• Player of Games
• Retrieval-Augmented Reinforcement Learning

The big picture

AI scaling

Tier 1

• Scaling Laws for Neural Language Models
• Takeoff speeds
• The Bitter Lesson

Tier 2

• AI and compute
• Scaling Laws for Transfer
• Training Compute-Optimal Large Language Models (Chinchilla)

Tier 3

• ✨ Pre-training under Infinite Compute
• Emergent Abilities of Large Language Models
• Transcending Scaling Laws with 0.1% Extra Compute (U-PaLM)

Tier 4+

• Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws
• Power Law Trends in Speedrunning and Machine Learning
• Scaling laws for single-agent reinforcement learning
• Beyond neural scaling laws: beating power law scaling via data pruning
• Emergent Abilities of Large Language Models
• Scaling Scaling Laws with Board Games

AI safety

Tier 1

• Three impacts of machine intelligence
• What failure looks like
• Without specific countermeasures, the easiest path to transformative AI likely leads to AI takeover

Tier 2

• An Overview of Catastrophic AI Risks
• Clarifying “What failure looks like” (part 1)
• Deep RL from human preferences
• The alignment problem from a deep learning perspective

Tier 3

• ✨ Alignment Faking in Large Language Models
• ✨ Constitutional Classifiers: Defending against Universal Jailbreaks
• ✨ Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs
• ✨ Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development
• Scheming AIs: Will AIs fake alignment during training in order to get power?

Tier 4+

• Towards a Law of Iterated Expectations for Heuristic Estimators
• Measuring Progress on Scalable Oversight for Large Language Models
• Scalable agent alignment via reward modelling
• AI Deception: A Survey of Examples, Risks, and Potential Solutions
• Benchmarks for Detecting Measurement Tampering
• Chess as a Testing Grounds for the Oracle Approach to AI Safety
• Close the Gates to an Inhuman Future: How and why we should choose to not develop superhuman general-purpose artificial intelligence
• Model evaluation for extreme risks
• Responsible Reporting for Frontier AI Development
• Safety Cases: How to Justify the Safety of Advanced AI Systems
• Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
• Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure
• Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
• Tools for Verifying Neural Models' Training Data
• Towards a Cautious Scientist AI with Convergent Safety Bounds
• Alignment of Language Agents
• Eliciting Latent Knowledge
• Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
• Red Teaming Language Models with Language Models
• Risks from Learned Optimization in Advanced Machine Learning Systems
• Unsolved Problems in ML Safety

Economic and social impacts

Tier 2

• ✨ AI 2027
• ✨ Situational Awareness (Aschenbrenner)

Tier 3

• Explosive growth from AI automation: A review of the arguments
• Language Models Can Reduce Asymmetry in Information Markets

Tier 4+

• Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero
• Foundation Models and Fair Use
• GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
• Levels of AGI: Operationalizing Progress on the Path to AGI
• Opportunities and Risks of LLMs for Scalable Deliberation with Polis
• On the Opportunities and Risks of Foundation Models

Philosophy

Tier 2

• Meaning without reference in large language models

Tier 4+

• Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
• Philosophers Ought to Develop, Theorize About, and Use Philosophically Relevant AI
• Towards Evaluating AI Systems for Moral Status Using Self-Reports

Maintainer

andreas@elicit.com