This market predicts whether OpenAI will release a technical report on a language model specifically designed for AI alignment research, with a focus on interpretability benchmarks, by December 31, 2024.

Resolves YES if:

• OpenAI publishes a technical report on or before January 1, 2025, detailing a model developed with the primary purpose of AI alignment research. The report must include benchmarks evaluating the model's interpretability.

Resolves PROB if:

• There is significant controversy or disagreement over whether the released report meets the criteria for AI alignment research and interpretability benchmarks.

Resolves NO if:

• OpenAI does not publish a technical report meeting the above criteria by January 1, 2025.

Definitions:

• A language model is an algorithm that processes and generates human language by assigning probabilities to sequences of tokens (words, characters, or subword units) based on learned patterns from training data. They can then be used for various natural language processing tasks, such as text prediction, text generation, machine translation, sentiment analysis, and more. Language models use statistical or machine learning methods, including deep learning techniques like recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer architectures, to capture the complex relationships between words and phrases in a language. Model must not have been released before this market's creation. While performance is not the primary goal, it must be competitive on benchmarks with language models at most 2 years behind it (this excludes the possibility of e.g. a Markov Chain being presented).

• "AI alignment research" refers to research focused on ensuring that artificial intelligence systems reliably understand and follow human intentions, values, and objectives, especially as AI systems become more capable and autonomous.

• "Interpretability benchmarks" refer to quantitative and/or qualitative evaluations designed to measure the clarity, explainability, and understandability of a model's outputs, internal workings, or decision-making processes.