Perplexity 发布 pplx-embed-v2-context-9b-preview 上下文嵌入模型

RT by @AravSrinivas: I’ve been convinced contextual embedding models were dope since voyage-context-3 was released. Started using them and benchmarking them, realized that, esp for long documents, I needed them to rank not just the answer chunk, but the disambiguating context chunks highly. Done well, this could allow you or your search agent to read only the relevant fraction of the document rather than every page of a long document. So I started trying to measure that too. Offered to share my benchmark results with the perplexity team sometime after they released their contextual model. It’s worth mentioning - not all modeling teams want feedback from third parties like me. The perplexity team was eager for it and within a week had a new model for me to benchmark. So I’d run their new checkpoint on my benchmark, stare at the results, get a little frustrated that the benchmark was imperfect and wasn’t measuring everything as well as I wanted it to, iterate on it a bit and give them ne

来源：X：Aravind Srinivas（Perplexity CEO） (@AravSrinivas) · x.lingyaoai.com