rateLLMiter is a Python rate limiter that smoothes out requests to LLM APIs to get faster, more consistent performance. It uses a ticket bucket algorithm rather than the usual exponential backoff.
It can't answer the questions without the limericks in the prompt. The benchmark is to establish how well it uses the context window. For example, I just asked it "What is sought by the English top brass?". The answer from the limerick is "Cranberry glass" and 4o answers correctly when given the associated limerick once out of 2500+ limericks.
However, without the limerick, 4o responded with: "The term "English top brass" typically refers to high-ranking officials or leaders within the British government, military, or other institutions. What they seek can vary widely depending on the context and the specific goals of their roles. Here are some general pursuits that might be sought by such individuals:
National Security: Ensuring the safety and security of the United Kingdom from internal and external threats is a primary concern. This involves defense strategies, intelligence operations, and counter-terrorism efforts.
Economic Stability: High-ranking officials often focus on policies and initiatives aimed at maintaining and improving the country’s economic health. This includes managing inflation, unemployment, trade relations, and economic growth.
Political Influence: Top brass often seek to maintain or expand their influence both domestically and internationally. This can involve diplomacy, forming alliances, and participating in international organizations like the United Nations or NATO.
Social Cohesion: Ensuring social stability and addressing issues such as inequality, healthcare, education, and social services are critical. This can involve implementing policies that promote social welfare and cohesion.
Public Policy Implementation: Leaders are responsible for developing and implementing policies that reflect the government’s priorities. This includes legislation, regulatory frameworks, and public administration.
Technological Advancement: Keeping the nation at the forefront of technological innovation is often a priority. This includes investments in research and development, supporting tech industries, and ensuring cybersecurity.
Environmental Sustainability: Addressing climate change and promoting sustainable practices are increasingly important. This includes policies aimed at reducing carbon emissions, protecting natural resources, and transitioning to renewable energy sources.
Cultural and Heritage Preservation: Protecting and promoting the country’s cultural heritage and national identity can also be a focus. This includes supporting the arts, preserving historical sites, and promoting cultural initiatives.
These pursuits are shaped by the current political climate, global trends, and the specific priorities of the leaders in question. Would you like more detailed information on any of these areas?"
I tested the LLMs to make sure they could not answer the questions unless the limerick was given to them. Other than 4o, they do very badly on this benchmark, so I don't think the test is invalidated by their training.
The reason I made Needle in a needlestack is the LLMs are getting to good at needle in a haystack. Until GPT-4o, no model was good at the NIAN benchmark.
If you ask the questions without providing the limerick first, it never gets the right answer. When the LLM gets the wrong answer, it is usually because it reverts to its training data and gives a generic answer that doesn't apply to the limerick.
I was thinking about something similar -- to make part of the question be sufficient information that the LLM can find the limerick. Then the 2nd part would ask something that would require a deeper understanding of the limerick (or other text).
I had the same experience. With a 16k prompt, Turbo was nearly flawless. But it wasn't very good at 32k and not usable at 100+. You have to repeat information to get good results with longer prompts