The state of Hawking radiation is non-classical

We show that the state of the Hawking radiation emitted from a large Schwarzschild black hole (BH) deviates significantly from a classical state, in spite of its apparent thermal nature. For this state, the occupation numbers of single modes of massless asymptotic fields, such as photons, gravitons and possibly neutrinos, are small and, as a result, their relative fluctuations are large. The occupation numbers of massive fields are much smaller and suppressed beyond even the expected Boltzmann suppression. It follows that this type of thermal state cannot be viewed as classical or even semiclassical. We substantiate this claim by showing that, in a state with low occupation numbers, physical observables have large quantum fluctuations and, as such, cannot be faithfully described by a mean-field or by a WKB-like semiclassical state. Since the evolution of the BH is unitary, our results imply that the state of the BH interior must also be non-classical when described in terms of the asymptotic fields. We show that such a non-classical interior cannot be described in terms of a semiclassical geometry, even though the average curvature is sub-Planckian.Comment: Replaced to agree with the published version. Added explanation

On the Entropy of Strings and Branes

We show that the entropy of strings that wind around the Euclidean time circle is proportional to the Noether charge associated with translations along the T-dual time direction. We consider an effective target-space field theory which includes a large class of terms in the action with various modes, interactions and $\alpha'$ corrections. The entropy and the Noether charge are shown to depend only on the values of fields at the boundary of space. The classical entropy, which is proportional to the inverse of Newton's constant, is then calculated by evaluating the appropriate boundary term for various geometries with and without a horizon. We verify, in our framework, that for higher-curvature pure gravity theories, the Wald entropy of static neutral black hole solutions is equal to the entropy derived from the Gibbons-Hawking boundary term. We then proceed to discuss horizonless geometries which contain, due to the back-reaction of the strings and branes, a second boundary in addition to the asymptotic boundary. Near this ``punctured'' boundary, the time-time component of the metric and the derivatives of its logarithm approach zero. Assuming that there are such non-singular solutions, we identify the entropy of the strings and branes in this geometry with the entropy of the solution to all orders in $\alpha'$. If the asymptotic region of an $\alpha'$-corrected neutral black hole is connected through the bulk to a puncture, then the black hole entropy is equal to the entropy of the strings and branes. Later, we discuss configurations similar to the charged black p-brane solutions of Horowitz and Strominger, with the second boundary, and show that, to leading order in the $\alpha'$ expansion, the classical entropy of the strings and branes is equal exactly to the Bekenstein-Hawking entropy. This result is extended to a configuration that asymptotes to AdS.Comment: 47 pages, 1 figure, 1 table, 1 appendix, published versio

What Makes a Language Easy to Deep-Learn?

Neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to produce forms for new meanings systematically. However, unlike humans, neural networks notoriously struggle with systematic generalization, and do not necessarily benefit from compositional structure in emergent communication simulations. This poses a problem for using neural networks to simulate human language learning and evolution, and suggests crucial differences in the biases of the different learning systems. Here, we directly test how neural networks compare to humans in learning and generalizing different input languages that vary in their degree of structure. We evaluate the memorization and generalization capabilities of a pre-trained language model GPT-3.5 (analagous to an adult second language learner) and recurrent neural networks trained from scratch (analaogous to a child first language learner). Our results show striking similarities between deep neural networks and adult human learners, with more structured linguistic input leading to more systematic generalization and to better convergence between neural networks and humans. These findings suggest that all the learning systems are sensitive to the structure of languages in similar ways with compositionality being advantageous for learning. Our findings draw a clear prediction regarding children's learning biases, as well as highlight the challenges of automated processing of languages spoken by small communities. Notably, the similarity between humans and machines opens new avenues for research on language learning and evolution.Comment: 32 pages, major update: improved text, added new analyses, added supplementary materia

Generating Benchmarks for Factuality Evaluation of Language Models

Before deploying a language model (LM) within a given domain, it is important to measure its tendency to generate factually incorrect information in that domain. Existing factual generation evaluation methods focus on facts sampled from the LM itself, and thus do not control the set of evaluated facts and might under-represent rare and unlikely facts. We propose FACTOR: Factual Assessment via Corpus TransfORmation, a scalable approach for evaluating LM factuality. FACTOR automatically transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements. We use our framework to create two benchmarks: Wiki-FACTOR and News-FACTOR. We show that: (i) our benchmark scores increase with model size and improve when the LM is augmented with retrieval; (ii) benchmark score correlates with perplexity, but the two metrics do not always agree on model ranking; and (iii) when perplexity and benchmark score disagree, the latter better reflects factuality in open-ended generation, as measured by human annotators. We make our data and code publicly available in https://github.com/AI21Labs/factor