Llm

Combining deterministic NLP with LLM-as-Judge for robust evaluation Summary Evaluation Challenges Summary evaluation metrics sometimes fall short in capturing the qualities most relevant to assessing summary quality. Traditional machine learning for natural language processing (NLP) has covered a lot of ground in this area. Widely used measures such as ROUGE focus on surface-level token overlap and n-gram matches. While effective for evaluating lexical similarity, these approaches offer limited insight into aspects such as factual accuracy or semantic completeness [1]. ...

If you are a command-line fan and want to experiment with large language models (LLM), you will love AiChat. There are many popular graphical front ends for working with LLMs, such as OpenAI’s ChatGPT, and Anthropic’s Claude, but get ready for this little powerhouse for CLI lovers as it has many advanced and useful features. One such feature is an easy-to-use, out-of-the-box RAG feature (Retrieval Augmented Generation) useful for searching existing content. I’ve put together a small demo here that shows how easy it can be to use in a pinch. There are many use cases where such an approach is just the right size. ...

Large foundation models like GPT can classify text according to a well-crafted prompt instruction, and it’s remarkable how well they can do this, considering there has been no explicit training with labeled datasets. This has traditionally been done using machine learning models and logistic regression techniques. However, with generative model classification, we lose the ‘confidence level’ or the probability score of the prediction available in logistic regression. Traditional models like logistic regression provide a probability score for each class, indicating the model’s confidence level in its predictions. This confidence score is not just valuable; it’s essential for decision-making, as it helps users gauge how confident the model is about its classifications. While generative model responses may align well with the intended classification, we don’t directly get an explicit probability for each class. This can be a limitation, particularly in high-stakes applications where knowing the model’s confidence level is crucial. ...

You might want to test an expected response from a prompt sent to a large language model, but string comparisons will not help you. The inherent variability in large language model (LLM) responses will require you to find new ways to compare generated prompt results. There are a few reasons why a generated prompt result will not exactly match a prior result: the prompt itself may have changed, the model parameters may have changed, or the model’s inherent variability may inject a small amount of change in the results. ...

As I stood outside and looked at the neighborhood wasteland that post-July 4th left behind, the whiff of gunpowder still hanging in the air, I felt a burst of good neighbor energy flow through me, so I grabbed a broom. Sweeping up the street gave me time to think about the other chores I had for the day, including the writing of a new blog post, and I began to wonder how I could use ChatGPT to help me speed some things up. ...

Since transformer input is processed in parallel rather than serially, it is necessary to encode the relative positions of the input sequence of tokens in some way. The positional encoding in the transformer model uses sinusoidal functions to create a unique encoding for each position. In working through the article on Transformers, as described in the original paper “Attention is All You Need” by Vaswani et al., the following formulas are used to encode the PE tensor values: ...