20-Nov-24

Prompt

LLMs commonly prepend and finish the core text in their output with buffer remarks.

Here's what I mean by one:

"These additional questions further explore technical aspects, practical applications, ethical concerns, and future possibilities related to large language models (LLMs)."

Do these aspects of inference have a name?

Output

The phenomenon you're describing, where large language models (LLMs) prepend or append their core output with additional remarks or context, can be understood as part of meta-commentary or meta-textual generation. These are not formal terms but describe the behavior where LLMs generate text that frames, explains, or contextualizes the core content. This behavior is often a result of how the model interprets the task or prompt, especially when it is uncertain about the exact boundaries of the expected output.

Here are some relevant concepts that might explain why this happens:

1. Buffer of Thoughts (BoT)

The Buffer of Thoughts (BoT) approach enhances reasoning in LLMs by introducing high-level thought templates that help organize and structure responses. This method involves a meta-buffer that stores these templates, which can be retrieved and instantiated during problem-solving tasks. The BoT approach aims to improve reasoning accuracy and robustness by ensuring that LLMs follow a structured thought process[2][4]. This structured reasoning could lead to the generation of additional explanatory text before or after the core response, as part of the thought-augmented reasoning process.

2. Chain-of-Thought Prompting (CoT)

Another related concept is Chain-of-Thought (CoT) prompting, where LLMs are encouraged to generate intermediate reasoning steps before arriving at a final answer[1]. This technique helps LLMs break down complex tasks and could result in additional framing or commentary around the main answer, especially if the model is trying to clarify its reasoning process.

3. Inference-Time Watermarking

In some cases, LLM outputs may include additional remarks due to techniques like inference-time watermarking, which manipulates token selection during text generation[5]. While this primarily focuses on embedding watermarks into generated text, it also highlights how inference-time processes can influence the structure and content of an LLM's output.

4. Stop Words and Output Control

LLMs also use mechanisms like stop words to control when they stop generating text[1]. If stop words are not carefully defined, the model might continue generating additional remarks after completing what it perceives as the core task. This could explain why some outputs include buffer-like statements at the beginning or end.

Conclusion

While there isn't a single formal term specifically for "buffer remarks" in LLM outputs, this behavior can be attributed to various factors like meta-commentary, thought-augmented reasoning (BoT), chain-of-thought prompting (CoT), and inference-time controls. These techniques aim to enhance reasoning and ensure coherent outputs but may sometimes result in extra framing text around the core content.