Tokenization for language modeling: Byte Pair Encoding vs Unigram Language Modeling

Tokenizers used by the best-performing language models (Bert, GPT-2, etc.) poorly reflect the morphology of English text. I had hoped to use some quarantine time to design one that more closely aligns to relationships between wordforms. But Kaj Bostrom and Greg Durrett beat me to it and so this blog post materialized instead. I add some additional motivation, evaluate both methods against ‘gold standard’ tokenizations, and speculate about what might come next.

Andrey Markov & Claude Shannon Counted Letters to Build the First Language-Generation Models

Shannon’s said: “OCRO HLI RGWR NMIELWIS”

GitHub - edwko/OuteTTS: Interface for OuteTTS models.

Interface for OuteTTS models. Contribute to edwko/OuteTTS development by creating an account on GitHub.

Top NLP Techniques Every Data Scientist Should Know by khushnuma khan on Blogulr!

Natural Language Processing (NLP) is a critical component of data science, especially given the surge in textual data from sou

Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs

Most interpretability research in NLP focuses on understanding the behavior and features of a fully trained model. However, certain insights into model behavior may only be accessible by observing the trajectory of the training process. We present a case study of syntax acquisition in masked language models (MLMs) that demonstrates how analyzing the evolution of interpretable artifacts throughout training deepens our understanding of emergent behavior. In particular, we study Syntactic Attention Structure (SAS), a naturally emerging property of MLMs wherein specific Transformer heads tend to focus on specific syntactic relations. We identify a brief window in pretraining when models abruptly acquire SAS, concurrent with a steep drop in loss. This breakthrough precipitates the subsequent acquisition of linguistic capabilities. We then examine the causal role of SAS by manipulating SAS during training, and demonstrate that SAS is necessary for the development of grammatical capabilities. We further find that SAS competes with other beneficial traits during training, and that briefly suppressing SAS improves model quality. These findings offer an interpretation of a real-world example of both simplicity bias and breakthrough training dynamics.

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GPT-4 API by OpenAI Now Available – Analytics India Magazine #GPT4API

OpenAI announces general availability for GPT-4 API and plans to remove older models in Completion API by next yearOpenAI announces general availability for GPT-4 API and plans to remove older models in Completion API

Chat-GPT: The Unnoticed Transformation of Your Thought Process #AIRevolution

When people strongly believe generative AI programs to be knowledgeable and confident, they are more likely to put their trust in them.

Improving Dialogue through Optimization of Language Models #DialogueOptimization

ChatGPT, developed by OpenAI, became a highly popular technology in 2022 due to its impressive capabilities in language optimization for dialogue generation. It offers a human-like conversational experience and has gained over 100 million users within a short period of time. ChatGPT is trained to respond in a conversational manner, understand queries, and even respond with emotions. It is optimized through various models such as Large Language Models, Supervised Fine Tuning, Reinforcement Learning from Human Feedback, and the Reward Model. These models help improve the efficiency, accuracy, and quality of ChatGPT's responses.

Revealing the structure of language model capabilities

Building a theoretical understanding of the capabilities of large language models (LLMs) is vital for our ability to predict and explain the behavior of these systems. Here, we investigate the structure of LLM capabilities by extracting latent capabilities from patterns of individual differences across a varied population of LLMs. Using a combination of Bayesian and frequentist factor analysis, we analyzed data from 29 different LLMs across 27 cognitive tasks. We found evidence that LLM capabilities are not monolithic. Instead, they are better explained by three well-delineated factors that represent reasoning, comprehension and core language modeling. Moreover, we found that these three factors can explain a high proportion of the variance in model performance. These results reveal a consistent structure in the capabilities of different LLMs and demonstrate the multifaceted nature of these capabilities. We also found that the three abilities show different relationships to model properties such as model size and instruction tuning. These patterns help refine our understanding of scaling laws and indicate that changes to a model that improve one ability might simultaneously impair others. Based on these findings, we suggest that benchmarks could be streamlined by focusing on tasks that tap into each broad model ability.