combinations, capturing a form of compositionality. For example, in some such models, if is the function that maps a word to its -d vector representation, then where ≈ is made precise by stipulating that its right-hand side must be the nearest neighbor of the value of the left-hand side. Other A positional language model assesses the probability of given words occurring close to one another in a text, not necessarily immediately adjacent. Similarly, bag-of-concepts models leverage the semantics associated with multi-word expressions such as buy_christmas_present, even when they are used in information-rich sentences like "today I bought a lot of very nice Christmas presents". Despite the limited successes in using neural networks, authors acknowledge the need for other techniques when modelling sign languages. Notable language models Notable language models, include: Pathways Language Model (PaLM), 540 billion parameter model, from Google Research Generalist Language Model (GLaM), 1 trillion parameter model, from Google Research Language Models for Dialog Applications (LaMDA), 137 billion parameter model from Google Research Megatron-Turing NLG, 530 billion parameter model, from Microsoft/Nvidia DreamFusion/Imagen, 3D image generation from Google Research Get3D from Nvidia MineClip from Nvidia BLOOM: BigScience Large Open-science Open-access Multilingual language model with 176 billion parameters. Generative pre-trained transformer (GPT) GPT-2: Generative Pre-trained Transformer 2 with 1.5 billion parameters GPT-3: Generative Pre-trained Transformer 3, with the unprecedented size of 2048-token-long context and 175 billion parameters (requiring 800 GB of storage) GPT-3.5/ChatGPT/InstructGPT from OpenAI GPT-NeoX-20B: An open source autoregressive language model with 20 billion parameters BERT: Bidirectional Encoder Representations from Transformers (BERT) OPT-175B by Meta AI, another 175-billion-parameter language model. It is available to the broader AI research community. Point-E by OpenAI, a 3D model generator RT-1 by Google, a model for operating robots ERNIE-Code by Baidu, a 560m parameter multilingual coding model VALL-E text to speech synthesis based on 3-second speech samples. It was pre-trained on 60,000 hours of English speech from 7,000 unique speakers (dataset: LibriLight). BLOOM 175B parameter open source LM that works in 46 languages and 13 programming languages Evaluation and benchmarks Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language-oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typically intended to be dynamic and to learn from data it sees, some proposed models investigate the rate of