The hermeneutic circle: a key to critical reading

The hermeneutic circle: a key to critical reading

In his book Being and Time, German philosopher Martin Heidegger introduced the concept of the hermeneutic circle. According to Heidegger, our understanding of a text as a whole is based on our understanding of each individual part, as well as our understanding of how each individual part refers to the whole text. This idea highlights the importance of context in our interpretation and comprehension of written material.

To practice critical reading effectively, it is crucial to consider the context in which a text is read. Before diving into a text, take a moment to reflect on your expectations, existing beliefs, and prior knowledge about the topic. By acknowledging these factors, you can approach the text with a more informed and open mindset. This awareness of your own context will help shape your interpretation and understanding of the material.

Additionally, the hermeneutic circle suggests that interpreting a text is not a linear process. Just as in a game app where players start with a small town and progress to a bigger and more developed place, our understanding of a text evolves as we make progress in our reading. Re-reading the content after finishing it can provide new perspectives and insights due to the updated context. This sense of progress is similar to how players feel a sense of accomplishment when they return to the initial place and see how much they have advanced. By comparing the initial and final states of our understanding, we can observe the progress we have made.

Incorporating these ideas into our approach to reading can enhance our critical thinking skills and deepen our understanding of texts. By being mindful of our context, actively engaging with the material, and embracing the iterative nature of interpretation, we can fully embrace the hermeneutic circle and unlock new layers of meaning within the text.

ChatGPT: Optimizing Language Models for Dialogue

ChatGPT, an advanced language model, has been optimized for dialogue. This format allows ChatGPT to not only provide answers but also engage in follow-up questions, admit mistakes, challenge incorrect premises, and reject inappropriate requests. The development of ChatGPT involved training the model using Reinforcement Learning from Human Feedback (RLHF), following similar methods as InstructGPT.

The training process for ChatGPT began with supervised fine-tuning. Human AI trainers played both sides of a conversation, acting as both the user and an AI assistant. These conversations served as the initial training data for the model. To further refine the model's responses, alternative completions of model-written messages were sampled and ranked by AI trainers. These rankings formed the basis for reward models, which were then used to fine-tune the model through Proximal Policy Optimization.

It is important to note that ChatGPT is part of the GPT-3.5 series and was trained on an Azure AI supercomputing infrastructure. Despite its capabilities, ChatGPT does sometimes generate plausible-sounding but incorrect or nonsensical answers. Addressing this issue is challenging due to several reasons. Firstly, during RL training, there is currently no definitive source of truth to guide the model. Secondly, training the model to be more cautious can cause it to decline questions that it could have answered correctly. Lastly, supervised training can mislead the model as the ideal answer may depend on the model's knowledge rather than the human demonstrator's knowledge.

Ideally, the model would ask clarifying questions when faced with ambiguous queries from users. However, the current models often resort to guessing the user's intended meaning. This highlights the ongoing efforts to improve the dialogue capabilities of language models like ChatGPT.

By understanding the training process and limitations of ChatGPT, users can approach interactions with the model with a critical mindset. Recognizing that the model's responses are not infallible and may require further clarification can help users navigate and utilize ChatGPT more effectively.

Connecting the Concepts

Although the hermeneutic circle and ChatGPT seem unrelated at first glance, there is an underlying connection between the two. Both concepts emphasize the importance of context and iterative understanding.

In the case of the hermeneutic circle, understanding a text as a whole requires grasping the individual parts and their relationships to the whole. Similarly, ChatGPT's training involves refining responses iteratively, taking into account feedback and alternative completions to optimize dialogue capabilities.

Both the hermeneutic circle and ChatGPT highlight the need for multiple engagements with the material. In critical reading, re-reading a text allows for a deeper understanding, as one's context has evolved throughout the reading process. Similarly, ChatGPT's development involves training the model through multiple iterations, incorporating feedback and fine-tuning to improve its responses.

Three Actionable Advice

1. Embrace the hermeneutic circle: When approaching a text, be aware of your own context, expectations, and prior knowledge. This awareness will shape your interpretation and understanding of the material. Allow yourself to re-read the text to gain new perspectives and insights as your context evolves.

2. Engage critically with ChatGPT: Recognize the limitations of language models like ChatGPT. Understand that its responses are not infallible and may require further clarification. Approach interactions with the model with a critical mindset, questioning and verifying its answers when necessary.

3. Provide feedback for improvement: If you encounter incorrect or nonsensical answers from ChatGPT, provide feedback to help refine and enhance the model's capabilities. This feedback contributes to the ongoing efforts to optimize language models for dialogue and improve their understanding.

Conclusion

The hermeneutic circle and ChatGPT may appear to be unrelated concepts, but they share common themes of context, iterative understanding, and the importance of engagement. By incorporating the principles of the hermeneutic circle into our reading practices and approaching ChatGPT interactions with a critical mindset, we can deepen our understanding of texts and optimize the dialogue capabilities of language models. Understanding the nuances of these concepts empowers us to navigate the complexities of interpretation and utilize language models more effectively.