Novel Uses For Large Language Models #
Here is a list of some novel uses for LLMs, in no particular order.
I wrote the original list items; the expanded details are written by GPT-4 with some editing by myself.
- Act as conversation practice partner for humans with social anxiety
An LLM can simulate real-life social interactions, offering a safe environment for individuals with social anxiety to practice conversations. The LLM can adapt its responses to mimic various social contexts and personalities, providing users with a range of interactive scenarios. - Perform automated brainstorming to generate and assess interesting ideas
The LLM would be configured to first generate a broad range of creative ideas. Following the ideation phase, the LLM would employ a set of algorithms to evaluate the feasibility, innovation, and potential impact of each idea, and present the most promising ideas to the user. - Infohazard assessor - use an LLM-powered agent to ingest an infohazard and determine whether it is safe to read
The infohazard assessor would automatically scan and analyze text to evaluate potential psychological or societal harms. If deemed risky, the system would provide a summary of the content's nature and its potential impacts without exposing the raw text. Note - this is similar to the usage of LLM agents as arbiters for zero-knowledge information exchange. - Long text summarizer - summarize a long blog post to extract key takeaways, or tell whether it's worth reading in its entirety
An LLM could be used to analyze and condense extensive blog posts into concise summaries, highlighting essential points and takeaways. This functionality would be particularly useful for readers looking to efficiently sift through large volumes of information, enabling them to focus on articles that align closely with their interests or needs. - Simulate conversations between humans to determine compatibility for friendships or romantic relationships
The LLM would simulate interactive dialogues between two users by adopting their communication styles, interests, and preferences based on their input data. The model would generate hypothetical conversations on various topics to explore how the participants might interact in real-life scenarios, gauging compatibility based on the responses in the dialogue. - Simulate conversations with dead loved ones
The LLM would ingest voice recordings, letters, and other personal data to model the linguistic style, tone, and typical responses of the deceased individual. This would enable users to engage in simulated conversations with the loved on, providing a sense of connection and closure. - Generate social media agents to shift Overton window on a given topic
Developers could create social media agents programmed to engage in discussions on specific topics, subtly introducing and emphasizing viewpoints that currently lie outside the Overton window. These agents would interact with users using a pre-defined strategy to gradually normalize previously fringe ideas, thereby shifting public perception and dialogue. - Textual "voice changer" - pass writing through an LLM to change writing style
The LLM would analyze the input text to understand its structure and meaning, then rephrase it to reflect the chosen style while preserving the original intent and information. This could be done to mimic a certain identity (e.g. change British English to American English) or to disguise alts by thwarting similarity analysis between texts. - Simulate debates to determine what lines of argument will be most convincing
This would involve setting up simulations where the LLM plays the role of a skeptic that needs to be convinced of an idea. Users could input a variety of arguments, and the LLM would process these to generate persuasive responses and rebuttals. - Simulate an interviewer in order to write blog posts via conversation
An LLM programmed to simulate an interviewer could generate dynamic conversations by posing tailored questions based on the topic of the blog post. The model would adapt its questioning in real-time, responding to previous answers to delve deeper into the subject. - Simulate an interviewer in order to poke holes in a potential theory
The model would be equipped to ask probing questions that challenge the assumptions, methodologies, and conclusions of the theory, acting like a devil’s advocate. This process would help theorists strengthen the overall robustness of the theory before it is presented in academic or professional settings. - Analyze tweets or posts in order to flag or extract personal information
The LLM could recognize various forms of personal information, such as names, phone numbers, and other sensitive identifiers within social media content. Upon detection, a system could automatically flag these posts for review or privacy alerts. - Act as moderator layer to flag prohibited LLM outputs
The LLM can function as a moderator that evaluates the outputs of another LLM before they are displayed to the user. This system would use predefined criteria to identify and flag content that is inappropriate, such as hate speech, biased statements, or confidential information leaks. - Act as personalized comforter or reassurer / "proactive" therapist that intervenes as necessary
An LLM could be integrated into a mental health app that monitors user inputs, such as diary entries and social media activity. By analyzing changes in mood or stress levels, the LLM can proactively initiate supportive conversations, offering coping strategies tailored to the individual's current emotional state. - Act as automated trip sitter / psychological stabilizer
The LLM would be customized to monitor the user's emotional state through conversation during a psychedelic experience. The model would provide calming narratives, guided breathing exercises, or engaging distractions based on the user’s needs. - Act as self-simulator to reply to emails when out of office / self FAQ
An LLM could be customized to understand and mimic an individual's specific communication style and preferences. Once configured, it would automatically manage incoming emails during the person's absence, providing timely and contextually appropriate responses based on past interactions and predefined guidelines. - Expound on lists of ideas to fill in the details of each idea
An LLM can function as an idea expander tool where users input concise lists of raw ideas. The model would then analyze each idea, generating detailed explanations, potential applications, and further development steps for each concept.
Page last updated: May 12, 2024