/explore
|
You are here 
|
artificialanalysis.ai | ||
| | | | |
www.vellum.ai
|
|
| | | | | Understand the latest benchmarks, their limitations, and how models compare. | |
| | | | |
www.javaadvent.com
|
|
| | | | | If you're reading this, you're probably already using some LLM for coding. Maybe it's Copilot, maybe Claude Code, maybe Cursor with Gemini enabled (or Cursor's own model). You know the drill. Do you truly expect the announcement "We are worst than competitors?" The problem is that when someone asks, "Which model is best for Java?", [...] | |
| | | | |
lilianweng.github.io
|
|
| | | | | Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes. Here, I would like to narrow down the problem of hallucination to cases where the model output is fabricated and not grounded by either the provided context or world knowledge. There are two types of hallucination: In-context hallucination: The model output should be consistent with the source content in context. Extrinsic hallucination: The model output should be grounded by the pre-training dataset. However, given the size of the pre-training dataset, it is too expensive to retrieve and identify conflicts per generation. If w... | |
| | | | |
www.jerpint.io
|
|
| | | A collection of anything and everything. | ||
