The chain of thought (CoT) details panels shown below appeared in the chats described in Sessions with ChatGPT on modeling patent claims/litigation and on reverse engineering AI models.
Other CoT details are shown in other pages on this site, including those on walking neural networks, Part 1 and Part 2.
In ChatGPT, clicking on a blurb like “Thought for 16 seconds” displays the “Details” panel on the right-hand side.
It’s open to question whether these prose statements — even if appearing in real time over the course of 16 seconds while the user waits for the AI chatbot’s response — accurately reflect an internal stream of reasoning steps. The ones I’ve seen appear to be linear, without back-tracking, for example, nor the rejection of multiple options. Though perhaps that is the actual stream.
See research paper “Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting“.
Also see article on the use of human-interpretable prose in CoT: “When working through a problem, OpenAI’s o1 model will write a chain-of-thought (CoT) in English. This CoT reasoning is human-interpretable by default, and I think that this is hugely valuable. Assuming we can ensure that these thoughts are faithful to the model’s true reasoning, they could be very useful for scalable oversight and monitoring. I’m very excited about research to help guarantee chain-of-thought faithfulness. However, there’s a impending paradigm for LLM reasoning that could make the whole problem of CoT faithfulness obsolete (and not in a good way).” The impending paradigm is that described in a paper from Meta researchers on COCONUT (Chain of Continuous Thought).
Thought for 28 seconds [a fairly pedestrian example, and 28 seconds is surprising for what sounds so straightforward, but useful to see how ChatGPT describes its “chain of thought”]:
Converting CSV data
OK, let me see: transforming a CSV file with over 660 columns into a new format. The first three columns hold patent details, while columns from the fourth onward showcase various CPC patent classifications, often marked by zeros.
Mapping out the script
I’m focusing on a Python script to restructure the CSV file. Columns 1, 2, and 3 remain unchanged, with column 3 quoted. Column 4 will list CPC codes, separated by semicolons.
Piecing together CSV transformation
Starting with reading the CSV file. Maintaining id, date, and text columns, and collecting CPC codes for rows with “1”s for the fourth column onward. Then, outputting a new CSV.
Navigating CSV fields
Ensuring text fields are quoted correctly using Python’s csv module to manage commas and quotes. This approach streamlines the reading and writing process for CSV files.
Crafting the output
I’m detailing the coding process: reading headers, preserving columns 1-3, gathering CPC codes, and writing to a new CSV. Also, ensuring the text field remains enclosed in quotes.
Aligning CPC codes
I’m pulling together which CPC codes (H04R and G10K) match ‘1’ values in the CSV. This involves checking their exact positions within the data to ensure accuracy.
Mapping and enhancing
The script is set to map ‘1’s to CPC codes, extract specific fields, and output a refined CSV, all while managing the large dataset efficiently.
Revising the script
I’m making tweaks to ensure ‘1’s map to CPC codes, text fields are quoted, and output is efficient. This approach balances accuracy with performance.
Ensuring accurate text quoting
I’m curious about the ‘csv’ module’s ‘quoting’ parameter to verify that text fields are consistently quoted in the output CSV, enhancing accuracy as requested.
