Work — Peterson Cheng

I had a rough idea of what I wanted to communicate. I used a cycle of outline-to-essay to refine my thoughts by pulling in new concepts slowly. This took place over the course of an hour or two; The final prompt:

Expand the following outline into a short essay in Markdown format with a button to copy the output. Each bullet point should be expanded into at least two paragraphs. When referencing any cognitive or behavioral science, please be sure to cite your sources.

Title - the Unique Advantages of LLMs and Cognitive Hypotheses

How existing recommendation/personalization systems work today
- Where the nature of choice lies and the loci of control
The impersonal monolith of personalization
- The bottleneck of impersonal interfaces to personalized content
- Due to the central tendency of large systems, despite personalization
- Economically is expensive to provide two interfaces to the same thing
- Self cannibalization of products (Gmail, Inbox) even though one may serve a minority cohort better but worse overall
The user's ability to dictate response structure
- Some considerations about working memory and the ability to immediately filter irrelevant information
- Transformative nature of parsing information quickly vs. choice paralysis
- The flexibility of response structure enables information flow to match the user mental model
- The timing of when relevant information is presented, not just the correctness- It's relation to chunking
The user's ability to iteratively refine a query
- Existing systems like faceted search
- Where they lack compared to the flexibility of LLMs
- The bidirectionality of information flow
The nature of almost correct answers
- Would Expect This to Be Bad
- By contrast, intermittent reinforcement makes it more rewarding to correct the answers
Potential applications

Everything below this line is the response from ChatGPT

How existing recommendation/personalization systems work today

Where the nature of choice lies and the loci of control

Existing recommendation and personalization systems are based on data-driven algorithms that collect and analyze user data, such as browsing history, preferences, and interactions, to provide users with personalized content. These systems, like those used by e-commerce platforms, social media, and content streaming services, aim to improve user experience by narrowing down the vast array of options to a manageable, relevant selection (Adomavicius & Tuzhilin, 2005).

However, the control over these recommendations often lies primarily with the system rather than the user. While users may have the option to provide feedback on the relevance or quality of the recommended content, their direct influence on the system's choices is limited. The locus of control, therefore, is largely centralized within the algorithmic system, which may not always align with the user's cognitive processes or preferences.

The impersonal monolith of personalization

The bottleneck of impersonal interfaces to personalized content

Personalization systems, despite their ability to tailor content to individual users, often face the challenge of presenting that content in a way that remains impersonal. Interfaces may be designed to accommodate the preferences of a majority of users but may not adequately account for the unique needs of individuals. This limitation can result in a suboptimal user experience, as the interface may not effectively communicate the value of the personalized content.

Due to the central tendency of large systems, despite personalization

Large systems, such as those employed by tech giants, often suffer from a tendency to cater to the central or average user. While these systems may be effective at personalizing content, they may not provide the level of customization or flexibility needed to create a truly personalized experience for individual users. This limitation can result in users feeling as though their needs are not being fully met, even as the system works diligently to curate content specifically for them.

Economically is expensive to provide two interfaces to the same thing

Developing multiple interfaces to accommodate a diverse array of user preferences can be costly and time-consuming. This often leads companies to choose a "one-size-fits-all" approach, sacrificing the potential benefits of a more personalized experience. While this strategy may be economically efficient, it can result in a suboptimal experience for users with unique preferences or needs.

Self cannibalization of products (Gmail, Inbox) even though one may serve a minority cohort better but worse overall

Product cannibalization occurs when a company's new product competes with and detracts from the sales of its existing products. This can be seen in the case of Google's Gmail and Inbox, where Inbox served a minority cohort better but was ultimately discontinued due to its overlap with Gmail. This decision highlights the challenges companies face when attempting to balance the needs of diverse user groups with the desire for overall market success.

The user's ability to dictate response structure

Some considerations about working memory and the ability to immediately filter irrelevant information

One of the key advantages of Large Language Models (LLMs) is their ability to provide users with more control over the structure of the information they receive. This is particularly relevant when considering cognitive processes like working memory, which is limited in capacity and duration (Cowan, 2001). By allowing users to dictate the response structure, LLMs can present information in a manner that aligns with the user's cognitive preferences, enabling them to filter out irrelevant information more efficiently.

Transformative nature of parsing information quickly vs. choice paralysis

The ability to quickly parse information and make decisions is essential for effective decision-making. When users are presented with too many choices, they may experience choice paralysis, which can lead to a decreased ability to make decisions and a reduced sense of satisfaction with the outcome (Schwartz, 2004). By allowing users to dictate the response structure in LLMs, they can access information more efficiently and avoid choice paralysis. This can lead to a more satisfying and productive experience for users.

The flexibility of response structure enables information flow to match the user mental model

Matching the information flow to the user's mental model is critical for effective communication and understanding. The flexibility of response structure provided by LLMs allows for this alignment, making it easier for users to absorb and process information. By tailoring the response structure to the user's cognitive preferences, LLMs can enhance comprehension and facilitate more meaningful engagement with the content.

The timing of when relevant information is presented, not just the correctness

The presentation of relevant information is not only about the content's correctness but also the timing at which it is delivered. LLMs can be designed to adapt to the user's pace, providing information when it is most needed and desired. This ability to adapt to the user's needs helps create a more personalized and engaging experience.

Its relation to chunking

Chunking is a cognitive strategy that involves organizing information into smaller, more manageable units to aid in memory and understanding (Miller, 1956). LLMs can incorporate chunking into their response structure by presenting information in bite-sized pieces, allowing users to process and remember the content more effectively. This is especially important when considering the limitations of working memory, as chunking can help users manage complex information more efficiently.

The user's ability to iteratively refine a query

Existing systems like faceted search

Faceted search is a popular technique that allows users to refine their search queries by selecting from multiple categories or filters. While this approach can help users narrow down their search results, it may not provide the level of flexibility and adaptability offered by LLMs.

Where they lack compared to the flexibility of LLMs

LLMs can enable users to iteratively refine their queries in a more natural and intuitive manner. By engaging in a back-and-forth dialogue with the LLM, users can provide feedback and ask follow-up questions, allowing the system to better understand and meet their needs. This interactive process can lead to more accurate and relevant results, surpassing the capabilities of traditional faceted search systems.

The bidirectionality of information flow

Bidirectionality refers to the reciprocal exchange of information between users and systems. In the context of LLMs, bidirectionality allows for a more dynamic and engaging user experience, as the system can adapt to the user's needs and preferences in real-time. This feature enables LLMs to provide highly personalized responses that are more likely to meet the user's expectations and requirements.

The nature of almost correct answers

Would Expect This to Be Bad

One might assume that providing almost correct answers would lead to confusion and frustration for users. However, LLMs can leverage this phenomenon to create a more engaging and rewarding experience.

By contrast, intermittent reinforcement makes it more rewarding to correct the answers

Intermittent reinforcement is a psychological principle that suggests that responses reinforced on a partial or unpredictable schedule are more resistant to extinction (Ferster & Skinner, 1957). In the context of LLMs, providing almost correct answers can serve as a form of intermittent reinforcement, encouraging users to engage with the system more deeply and take an active role in correcting the answers. This process can make the experience more rewarding and enjoyable for users.

Potential applications

LLMs hold significant potential across various domains, including education, healthcare, customer support, and content creation. In education, LLMs can provide personalized learning experiences by adapting to individual student needs and preferences. They can offer tailored explanations, answer questions, and provide feedback, creating a more effective learning environment.

In healthcare, LLMs can be used to provide medical professionals with diagnostic support, suggest treatment options, and offer up-to-date medical information. This can help doctors make more informed decisions and improve patient care.

Customer support can also benefit from LLMs, as they can handle a wide range of inquiries, provide real-time assistance, and adapt to the user's communication style. This can lead to more efficient and satisfying customer support experiences.

Content creation is another area where LLMs can be invaluable. By understanding the user's intent and preferences, LLMs can assist in generating high-quality content tailored to specific audiences or purposes. This can save time and effort for content creators while ensuring that the final product is engaging and relevant.

In conclusion, the unique advantages of LLMs, including the ability to dictate response structure, iteratively refine queries, and provide bidirectional information flow, offer significant potential for enhancing user experiences across a variety of applications. By leveraging cognitive hypotheses and adapting to user preferences, LLMs can provide highly personalized and engaging experiences that surpass those of traditional recommendation and personalization systems.

Using an LLM to organize my thoughts about LLMs