Can you explain how inflation affects interest rates and the broader economy?... According to the Taylor Rule, monetary policy should adjust the federal funds rate in response to deviations of actual inflation from the target rate and output from potential GDP. (User pauses to think and digest, while the LLM continues generating content) This increase in rates raises the cost of borrowing, reduces consumer spending and business investment, and can lead to slower economic growth ... Waiting User Normal User (Unaware of Change) Satisfied User How do I make olive oil garlic pasta?Sure! I'd be more than happy to help you with that.Olive oil garlic pasta is a wonderfully simple yet flavorful dish that's perfect for just about any occasion.(Streaming paused due to limited computational resource, leaving the user idle and disengaged) ... Can you explain how inflation affects interest rates and the broader economy?How do I make olive oil garlic pasta?... According to the Taylor Rule, monetary policy should adjust the federal funds rate in response to deviations of actual inflation from the target rate and output from potential GDP. (Streaming is slowed, allowing the user time to digest the content and freeing up computational resources) ... Sure! I'd be more than happy to help you with that.Olive oil garlic pasta is a wonderfully simple yet flavorful dish that's perfect for just about any occasion.To get started, you'll need a few basic ingredients: spaghetti, garlic, extra virgin olive oil, red pepper flakes, salt, and parsley... (Streaming continues smoothly thanks to sufficient computational resources, and proceeds at a faster pace since the content is low in complexity) Normal User Figure 1: (a) In regular LLM streaming, the streaming speed is not associated with the content being delivered.Complex content may be streamed faster than users can comfortably read, resulting in wasted resource.Conversely, simple content may be streamed too slowly, causing users to wait unnecessarily.(b) In our proposed approach, the streaming speed is adapted based on the estimated content complexity.Complex content is slowed down to support user comprehension and optimize resource usage, while simpler content is streamed more quickly to better align with the user's natural reading speed.

Recent advances in Large Language Models (LLMs) have demonstrated their potential as autonomous agents across various tasks. One emerging application is the use of LLMs in playing games. In this work, we explore a practical problem for the gaming industry: Can LLMs be used to measure game difficulty? We evaluate the feasibility of using LLM agents to test game difficulty, focusing on two widely played games: Wordle and Slay the Spire . Our results reveal an interesting finding: although LLMs may not perform as well as the average human player, their performance, when guided by simple, generic prompting techniques, shows a statistically significant and strong correlation with difficulty indicated by human players. This suggests that LLMs could potentially serve as human-like agents for measuring game difficulty during the development process, as their assessments may align closely with those of human players. Based on our experiments, we also propose general principles and guidelines for integrating LLMs into the game testing workflow.

Humor detection has gained attention in recent years due to the desire to understand usergenerated content with figurative language.However, substantial individual and cultural differences in humor perception make it very difficult to collect a large-scale humor dataset with reliable humor labels.We propose CHoRaL, a framework to generate perceived humor labels on Facebook posts, using the naturally available user reactions to these posts with no manual annotation needed.CHoRaL provides both binary labels and continuous scores of humor and non-humor.We present the largest dataset to date with labeled humor on 785K posts related to COVID-19.Additionally, we analyze the expression of COVIDrelated humor in social media by extracting lexico-semantic and affective features from the posts, and build humor detection models with performance similar to humans.CHoRaL enables the development of large-scale humor detection models on any topic and opens a new path to the study of humor on social media.