You’re not alone.

Chat models struggle to summarize more than a few messages at once primarily due to their inherent design limitations and the complexity of human language. These models process input sequentially, meaning they analyze text one piece at a time. When presented with large volumes of information, they can lose context or become overwhelmed, resulting in inaccuracies.

Additionally, summarization requires a deep understanding of nuance, intention, and underlying themes, which are challenging for AI to capture, especially when multiple messages interact or contradict each other. The model may fall into a pattern of generating content based on statistical probabilities rather than true comprehension, leading to hallucinations or irrelevant summaries.

Finally, limitations in the model's memory capacity prevent it from retaining all details over longer conversations, causing it to truncate key information or misrepresent the overall message. This combination of factors contributes to the difficulty chat models encounter in providing coherent and accurate summaries for extensive dialogue exchanges.

• Pattern Recognition: These models identify language patterns, but may combine parts of different messages instead of summarizing them accurately.

• Data Limitations: The training data can have gaps or biases. When summarizing, the model may fill these gaps with believable but false information.

• Language Complexity: Human language is intricate, and the model might misinterpret meanings, resulting in inaccuracies.

It can be hard to spot when a model invents information, as the output may appear logical and relevant. Users must critically assess the content to tell what's true and what's not.

Combining messages from different sources into a chat model is quite challenging. First, each source may use different formats like emails, texts, social media posts, or voice recordings. These formats can differ greatly in structure and details, making it hard to gather and unify the needed information.

Even if the messages are digital, public chat models often find it difficult to combine them. Many models are trained on specific datasets with similar formats, so they may not adapt well to varied inputs without significant preparation. This preparation involves cleaning the data, standardizing formats, and aligning different structures, which can be time-consuming and prone to mistakes.

Additionally, context is a concern. Messages from different sources may have meanings that don't transfer easily between platforms. What works in one context may not fit in another, complicating the model's ability to produce clear and contextually relevant replies.