August 2023 Summaries

Skeleton-of-Thought approach aims to reduce large language model latency while enhancing answer quality by guiding LLMs to construct answer skeletons before parallel content elaboration, achieving impressive speed-ups of up to 2.39x across 11 models. This innovative methodology is similar to writing an outline on a given topic and relies on the chain-of-thought approach that encourages generative AI to showcase its presumed logic when answering a question or solving a problem. The method is data-centric, relying on prompt engineering to accelerate off-the-shelf LLMs without any changes to their model or hardware. SoT has been tested across 11 models and shows significant speed-up potential for common sense knowledge generation, with some question types achieving higher relevance and diversity in answer quality. However, the approach struggles with math questions due to its reliance on context from previous steps, which is not applicable in step-by-step reasoning tasks like math problems. Future work aims to explore trigger mechanisms for specific question types, develop a graph-of-thought architecture that mimics human thought processes, and potentially replace the attention mechanism with alternative architectures. The approach has potential applications in general chatbot systems, improving user experience and lowering system costs by parallelizing content elaboration between segments of a question or multiple questions.

In this paper, the authors propose a method for extending the context window of pre-trained language models without any additional training or modification of the model architecture. The proposed method, called Positional Interpolation (PI), is based on the observation that positional embeddings in transformer models can be interpolated to extend the range of attention scores beyond the original sequence length. The authors first provide a mathematical analysis of why existing methods for extending context windows, such as RoPE, fail to generalize well outside the trained window size. They show that while positional embeddings are designed to capture relative positions within the sequence, they can lead to catastrophic issues when used beyond their intended range. To address this issue, the authors propose PI, which involves interpolating the pre-trained positional embeddings based on their relative positions in the extended window. This approach effectively extends the attention scores to cover the entire sequence, allowing the model to attend to tokens outside its original training context. The authors demonstrate the effectiveness of PI through a series of experiments on various language modeling tasks and benchmarks. They show that using PI with pre-trained Llama models can significantly improve performance on long context windows while maintaining or even improving performance on shorter contexts. Overall, this paper presents an elegant solution for extending the context window of transformer models without requiring any additional training or modification of the model architecture. The proposed method has the potential to enable new applications and improvements in various natural language processing tasks that require long-range dependencies and understanding of context.

The text provides a comprehensive guide on how to thrive during your first tech internship. It emphasizes the importance of networking at career fairs and hackathons, showing initiative in follow-ups and interviews, and maintaining a learning mindset throughout the process. The author also shares their experience working at Arize, an AI startup, highlighting the importance of embracing the company's culture, taking ownership of projects, collaborating with different teams, and enjoying the time spent learning new skills. The text concludes by acknowledging the support from the team at Arize and expressing gratitude for the opportunity to gain first-hand knowledge in AI and machine learning.

Modelbit and Arize's new integration allows for rapid deployment of machine learning (ML) models into production with just one line of code. This enables teams to monitor and fine-tune their ML models instantly, saving time and effort compared to building custom pipelines from scratch. The integration involves setting up a notebook environment, adding Arize keys to Modelbit, defining functions that log inference results to Arize, and deploying the inference function to Modelbit. With this integration, teams can now easily monitor, troubleshoot, and fine-tune their models running in production, as well as detect issues and automate model retraining. Both Modelbit and Arize offer free accounts for users to try out the integration.

This paper introduces Llama 2, a collection of pre-trained and fine-tuned large language models with parameters ranging from 7 billion to 70 billion. The fine-tuned model, Llama 2-Chat, is designed for dialogue use cases and showcases superior performance on various benchmarks. The authors emphasize the importance of safety considerations in large language models, highlighting the need for transparency in training data, human evaluations, and reinforcement learning with human feedback. They also discuss the potential trade-off between helpfulness and safety, suggesting that as a model becomes more helpful, it may become less safe. Llama 2 is released under an open license, allowing users to fine-tune the model on specific domains. The authors aim to promote the use of open-source models and encourage transparency in large language model development.