Show HN: Semlib – Semantic Data Processing
6 comments
·September 15, 2025Y_Y
>>> await sort(presidents, by="right-leaning")
['Jimmy Carter', 'Bill Clinton', 'George H. W. Bush', 'Ronald Reagan']
anishathalye
That was a small self-contained example that fit above the fold in the README (and fwiw even last year’s models like GPT-4o give the right output there). That `sort` is based on pairwise comparisons, which is one of the best ways you can do it in terms of accuracy (Qin et al., 2023: https://arxiv.org/abs/2306.17563).
I think there are many real use cases where you might want a semantic sort / semantic data processing in general, when there isn’t a deterministic way to do the task and there is not necessarily a single right answer, and some amount of error (due to LLMs being imperfect) is tolerable. See https://semlib.anish.io/examples/arxiv-recommendations/ for one concrete example. In my opinion, the outputs are pretty high quality, to the point where this is practically usable.
These primitives can be _composed_, and that’s where this approach really shines. As a case study, I tried automating a part of performance reviews at my company, and the Semlib+LLM approach did _better_ than me (don’t worry, I didn’t dump AI-generated outputs on people, I first did the work manually, and shared both versions with an explanation of where each version came from). See the case study in https://anishathalye.com/semlib/
There’s also some related academic work in this area that also talks about applications. One of the most compelling IMO is DocETL’s collaboration to analyze police records (https://arxiv.org/abs/2410.12189). Some others you might enjoy checking out are LOTUS (https://arxiv.org/abs/2407.11418v1), Palimpzest (https://arxiv.org/abs/2405.14696), and Aryn (https://arxiv.org/abs/2409.00847).
Y_Y
As you compose fuzzy operations your errors multiply! Nobody is asking for perfection, but this tool seems to me a straightforward way to launder bad data. If you want to do a quick check of an idea then it's probably great, but if you're going to be rigorous and use hard data and reproducible, understandable methods then I don't think it offers anything. The plea for citations at the end of the readme also rubs me the wrong way.
anishathalye
I think semantic data processing in this style has a nonempty set of use cases (e.g., I find the fuzzy sorting of arXiv papers to be useful, I find the examples in the docs representative of some real-world tasks where this style of data processing makes sense, and I find many of the motivating examples and use cases in the academic work compelling). At the same time, I think there are many tasks for which this approach is not the right one to use.
Sorry you didn't like the wording in the README, that was not the intention. I like to give people a canonical form they can copy-paste if they want to cite the work, things have been a mess for many of my other GitHub repos, which makes it hard to find who is using the work (which can be really informative for improving the software, and I often follow-up with authors of papers via email etc.). For example, I heard about Amazon MemoryDB because they use Porcupine (https://dl.acm.org/doi/pdf/10.1145/3626246.3653380). Appreciate you sharing your feelings; I stripped the text from the README; if you have additional suggestions, would appreciate your comments or a PR.
hobofan
Why not?
List-sorting/prioritizing list is among one of the best use-cases for LLMs, especially if the metrics for it are fuzzy, e.g. "what are the 10 sales lead of this list of 1000 that I should prioritize".
One of the more interesting approaches for that is arbitron[0], which does pairwise ranking with multiple metrics/agents to provide a multi-faceted sorting.
Instead of building a new data processing library, I would have offered only the novel NLP part and exposed it to existing libraries like pandas, polars, and spacy.
Does it batch requests?