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For an interview focused specifically on language modeling and related to AI GPTs, you should aim to have deep knowledge in the following categories within this topic:

1. Definition and Purpose of Language Models: Understand what a language model is, the different types of language models (unidirectional, bidirectional, generative), and their applications in various natural language processing tasks.

2. Perplexity: Know how perplexity is used as a measure of a language model's performance, how it is calculated, and what constitutes a good perplexity score in different contexts.

3. Tokenization: Be able to explain the process of breaking down text into tokens, the difference between word-level, subword-level (Byte Pair Encoding, WordPiece), and character-level tokenization, and their impact on model performance.

4. Sequence Prediction: Understand how language models predict sequences of text, including next-word prediction, and how this capability is fundamental to tasks like translation, summarization, and text generation.

5. Embeddings: Know about word embeddings (e.g., Word2Vec, GloVe) and contextual embeddings generated by models like GPT, their role in capturing semantic meaning, and how they are used in downstream tasks.

6. Attention Mechanism: Dive into how attention mechanisms, particularly self-attention in transformers, allow language models to weigh the importance of different tokens when generating text.

7. Transformer Architecture Details: Have detailed knowledge of the architecture of transformers, including how they differ from previous sequence models like RNNs and LSTMs, and why they are more effective for language modeling.

8. Pre-training Objectives: Understand the objectives used during the pre-training of language models, such as masked language modeling, causal language modeling, and next sentence prediction.

9. Fine-tuning Process: Be able to explain how GPT models are fine-tuned for specific tasks, the data requirements for fine-tuning, and strategies to prevent overfitting during this phase.

10. Generative vs. Discriminative Models: Know the difference between generative models like GPT (which generate text) and discriminative models (which classify or predict), and when to use each type.

11. Language Model Evaluation: Be familiar with the various ways to evaluate language models beyond perplexity, including using human evaluations, downstream task performance, and A/B testing.

12. Decoding Strategies: Understand different strategies for generating text from language models, such as greedy decoding, beam search, top-k sampling, and nucleus sampling, and the trade-offs of each method.

13. Scaling Laws for Language Models: Discuss the observed relationships between the size of language models, the amount of training data, and model performance, and what this implies for the development of future models.

14. Contextual Nuances in Language Modeling: Explain how language models handle context, ambiguity, and the subtleties of human language, including idioms, sarcasm, and pragmatics.

15. Mitigating Bias: Have a conversation about the challenges of bias in language models, how biases can propagate from training data to model outputs, and strategies for mitigating bias.

In preparation for your interview, you should not only understand these concepts but also be able to provide examples, discuss implications for software architecture decisions, and potentially talk about your own experiences or hypothetical scenarios involving language modeling with AI GPTs.