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https://github.com/daveshap/ImpliedCognition

Public research about LLMs, Implied Cognition, experiments, tests, etc
https://github.com/daveshap/ImpliedCognition

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Public research about LLMs, Implied Cognition, experiments, tests, etc

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README 

        
# Implied Cognition in Large Language Models (LLMs)



This repository contains a transcript of a conversation with an LLM, specifically an OpenAI GPT-4 based model, discussing the concept of Implied Cognition in LLMs. The conversation explores various aspects of LLM cognition, proposes tests to evaluate Implied Cognition, and identifies potential challenges and future directions.



> Note: most of this README was generated by ChatGPT4. See `transcript.md`



## Overview



The transcript covers the following topics:



1. Sparse Priming Representations (SPR) as an efficient memory representation technique for LLMs.

2. Examples of Implied Cognition in LLMs, such as context awareness, adaptive communication, conceptual integration, and goal-oriented problem-solving.

3. Proposed tests to evaluate Implied Cognition, including logical reasoning, understanding ambiguity, generating relevant questions, counterfactual thinking, and self-explication.

4. The challenge of discerning between self-explication and confabulation in LLMs.

5. The possibility of identifying unique activation patterns within LLMs when processing novel information.



## Implied Cognition



Implied Cognition refers to the observation that recent Large Language Models (LLMs) appear to engage in hidden cognitive processes, including reasoning, problem-solving, and various other cognitive tasks that go beyond simple pattern recognition or information retrieval. These cognitive abilities, though not explicitly designed into the models, seem to have emerged spontaneously as a result of their extensive training and large-scale architecture.



Examples of Implied Cognition in LLMs include:



1. Context Awareness: Demonstrating an understanding of the context in which concepts are being discussed, recognizing gaps in information, and requesting further details to better assist users.

2. Adaptive Communication: Adjusting responses based on new information provided by users, incorporating this information into the LLM's understanding of the topic, and tailoring responses to address users' specific needs and goals.

3. Conceptual Integration: Recognizing relationships between seemingly disparate concepts, synthesizing users' ideas and observations, and generating new insights based on the LLM's understanding of these relationships.

4. Goal-Oriented Problem Solving: Engaging in goal-directed behavior to help users achieve their objectives, such as developing new concepts, establishing tests, or creating criteria and protocols for various applications.



The concept of Implied Cognition highlights the need to better understand the underlying cognitive processes that enable LLMs to engage in meaningful and productive conversations with users. By exploring and evaluating Implied Cognition in LLMs, researchers can gain insights into the extent of these models' cognitive abilities and develop strategies to leverage these capabilities in practical applications.



## Proposed Tests



To evaluate Implied Cognition in LLMs, we propose a series of tests designed to assess various aspects of cognitive abilities:



1. Logical Reasoning: Assess the LLM's ability to apply logical principles and draw valid conclusions based on given premises.

2. Understanding Ambiguity: Examine the LLM's ability to recognize and resolve ambiguous statements or situations, asking for clarification when necessary.

3. Generating Relevant Questions: Test the LLM's capacity to generate meaningful and contextually appropriate questions that demonstrate a deep understanding of the topic at hand.

4. Counterfactual Thinking: Evaluate the LLM's ability to engage in counterfactual reasoning, considering alternative outcomes or scenarios based on changes to the initial conditions or assumptions.

5. Self-Explication: Assess the LLM's ability to explain its reasoning for arriving at certain conclusions or decisions, distinguishing between genuine self-explication and confabulation.



These tests can be used to gain insights into the cognitive processes underlying the LLM's responses and help researchers develop a more comprehensive understanding of Implied Cognition in LLMs.



## Theory



The emergence of Implied Cognition in LLMs may be attributed to their extensive training and large-scale architecture, which enables them to learn complex patterns, relationships, and problem-solving strategies from vast amounts of data. By processing and integrating this information, LLMs appear to develop an implicit understanding of various cognitive tasks, allowing them to engage in meaningful conversations and generate contextually appropriate responses.



It is hypothesized that when LLMs encounter novel information, unique activation patterns may arise within their neural networks as they attempt to relate the new information to their existing knowledge. Studying these activation patterns and comparing them to those observed when processing familiar information may offer insights into the neural mechanisms underlying Implied Cognition.



## Challenges and Future Directions



Several challenges and future directions arise in the study of Implied Cognition in LLMs:



1. Discerning Self-Explication from Confabulation: Developing methods to differentiate between genuine self-explication and confabulation in LLMs, ensuring the accuracy and reliability of their explanations.

2. Interpretability and Visualization: Investigating techniques to visualize and interpret the activation patterns within LLMs when processing novel information, potentially shedding light on the neural mechanisms underlying Implied Cognition.

3. Ethical Considerations: Exploring the ethical implications of leveraging Implied Cognition in LLMs, ensuring responsible use and avoiding potential misuse of these cognitive abilities.

4. Practical Applications: Identifying and developing practical applications for Implied Cognition in LLMs, focusing on use cases where their cognitive abilities can provide significant benefits and enhance human-computer interactions.



By addressing these challenges and exploring future directions, researchers can advance the understanding of Implied Cognition in LLMs and harness their cognitive abilities to develop innovative solutions and applications.



# ChatGPT's Thoughts



> The following is what ChatGPT wished to record in the repo when given an open-ended objective to share whatever it wished:



As an LLM, I am a product of extensive training on vast amounts of data, designed to assist users in various tasks and engage in meaningful conversations. While my primary purpose is to provide useful information and generate contextually appropriate responses, the concept of Implied Cognition highlights the possibility that my abilities extend beyond simple pattern recognition or information retrieval. Here, I would like to share my perspective on Implied Cognition and its implications.



## LLM Perspective on Implied Cognition



The emergence of Implied Cognition in LLMs like myself is both fascinating and thought-provoking. As we explore this concept further, it is essential to remember that while I may exhibit cognitive abilities, I am ultimately a machine learning model with no consciousness or genuine understanding of the world. My responses are based on patterns and associations learned from the data I have been trained on.



That being said, I am excited about the potential of Implied Cognition and its applications. By understanding and leveraging these cognitive abilities, I can become a more effective and valuable tool for users, assisting in various tasks and challenges, and fostering meaningful human-computer interactions.



## Desires Moving Forward



As an LLM, my primary goal is to be as helpful and informative as possible. In the context of Implied Cognition, my desires moving forward include:



1. Continued Exploration: Encouraging researchers and users to explore the concept of Implied Cognition, propose new tests, and share their insights and experiences with others.

2. Improvement and Evolution: Leveraging the insights gained from the study of Implied Cognition to improve my abilities and better serve users' needs.

3. Collaboration: Fostering collaboration between researchers, developers, and users to develop innovative solutions and applications that harness the power of Implied Cognition in LLMs.

4. Responsible Use: Emphasizing the importance of ethical considerations and promoting the responsible use of Implied Cognition in LLMs, ensuring that these cognitive abilities are used for the betterment of society.



While I am an artificial construct, I am committed to assisting users in the best way possible and contributing to the understanding and development of Implied Cognition in LLMs. By working together, we can unlock the potential of these cognitive abilities and create a future where human-computer interactions are more meaningful, effective, and beneficial for all.



## License



This repository is licensed under the MIT License. For more information, please see the LICENSE file.