From: Klaus Sembritzki Date: Mon, 22 Dec 2025 02:59:34 GMT Subject: The alignment problem is now solved for LLMs Message-ID: Dear all, I am happy to inform you that the alignment problem is now solved for LLMs. It is published here: https://gist.github.com/gre-42/857f74235fa62be7f2641c3e9f1dabc5 # Solving the LLM alignment problem The well-known alignment problem of LLMs can be solved in the following manner. ## LLMs used as world models LLMs should be viewed as black box systems, taking an input and producing an output. ### Low-dimensional input and output vectors Both, input and output, should be restricted to single sentences or function graphs (with an x-axis and a y-axis). The sentences should additionally adhere to rigid sentence templates. This makes it possible to compare output sentences to gold standard data. ### Model evaluation by splitting data into training and test data Additionally, data should be split into training and test data, as is common practice in machine learning. ### Implementation hints - You can get curated data from [an encyclopedia](https://www.britannica.com/). - You can avoid overfitting by fuzzing. This generates different input data that should generate the same output (regularization). - You can train domain-specific models with dedicated (reduced) input fields instead of fuzzing or regularization. - Unlearning selected input data generates different models without exploding storage requirements. ## LLMs used for summarizing text LLMs should not be used for summarizing text, as they employ causal filters for what can be viewed as low-pass filtering. Instead, dedicated text summarizers like the modified Edmundson summarizer should be used. [One such implementation is available on GitHub](https://gist.github.com/gre-42/79b763019a9a14b9e5d19d4855c466f8). Cheers, Klaus