[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Rohan Paul [@rohanpaul_ai](/creator/twitter/rohanpaul_ai) on x 73.8K followers Created: 2025-07-17 10:18:00 UTC Brilliant paper for optimizing your prompt-design. 💡 Keep crucial rules early in your prompt, break huge lists into chunks, and expect misses past XXX no matter how fancy the engine. This paper checks what happens when the rules or instruction list reaches XXX. IFScale, the benchmark, asks a model to write a business report while slipping in up to XXX exact keywords. Because scoring is plain keyword matching, the team charts accuracy for XX models from X vendors. Results show three decay shapes. Reasoning models like o3 stay near XXX% until about XXX rules then drop fast, gpt‑4.1 drifts down in a straight line, and smaller llama versions plunge early. Even the strongest system lands at XX% with XXX rules. The study also spots a primacy bias, so early keywords get more love once the list grows, and omissions overwhelm partial matches. More rules stretch response time, meaning teams must juggle speed against recall. ---- Paper – arxiv. org/abs/2507.11538 Paper Title: "How Many Instructions Can LLMs Follow at Once?"  XXXXXX engagements  **Related Topics** [lists](/topic/lists) [Post Link](https://x.com/rohanpaul_ai/status/1945790079290798453)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Rohan Paul @rohanpaul_ai on x 73.8K followers
Created: 2025-07-17 10:18:00 UTC
Brilliant paper for optimizing your prompt-design. 💡
Keep crucial rules early in your prompt, break huge lists into chunks, and expect misses past XXX no matter how fancy the engine.
This paper checks what happens when the rules or instruction list reaches XXX.
IFScale, the benchmark, asks a model to write a business report while slipping in up to XXX exact keywords.
Because scoring is plain keyword matching, the team charts accuracy for XX models from X vendors.
Results show three decay shapes.
Reasoning models like o3 stay near XXX% until about XXX rules then drop fast, gpt‑4.1 drifts down in a straight line, and smaller llama versions plunge early.
Even the strongest system lands at XX% with XXX rules.
The study also spots a primacy bias, so early keywords get more love once the list grows, and omissions overwhelm partial matches.
More rules stretch response time, meaning teams must juggle speed against recall.
Paper – arxiv. org/abs/2507.11538
Paper Title: "How Many Instructions Can LLMs Follow at Once?"
XXXXXX engagements
Related Topics lists