Show HN: Improving search ranking with chess Elo scores
39 comments
·July 16, 2025Alex3917
Out of curiosity, is there a reason why you are using ELO proper, rather than one of the ELO variants that doesn't make assumptions about the distribution of results? E.g.:
npip99
Hey! We actually did a lot of research into ELO consistency, i.e. to check whether or not the NxN pairwise matrix followed the ELO model. It was a long road that's probably grounds for an entirely separate blog post, but the TLDR is that we observe that:
For each document, there is a secret hidden score "s" which is the "fundamental relevance according to the LLM". Then, when we sample (q, d1, d2) from the LLM, the LLM follows the statistical property that:
- The "fundamental hidden preference" is `pref = s_{d1} - s_{d2}`, usually ranging between -4 and 4.
- The LLM will sample a normal distribution around the `pref` with stddev ~0.2, which is some "inner noise" that the LLM experiences before coming to a judgement.
- The preference will pass through the sigmoid to get a sampled_score \in [0, 1].
- There is an additional 2% noise. i.e., 0.98 * sampled_score + 0.02 * random.random()
When we use Maximum Likelihood Estimation to find the most likely predicted "hidden scores" \hat{s} associated with each document, then we go ahead and sample pairwise matrices according to `0.98 * sigmoid( \hat{s}_1 - \hat{s}_2 + N(0, 0.02) ) + Uniform(0.02)`, then we get a pairwise matrix with virtually identical statistical properties to the observed pairwise matrices.
pbhjpbhj
So this is for recruitment?
I like the pairwise approach but in the field I'm interested in, at the document level there can be a lot of relevance (we historically use scoring based on TF-IDF) but we tend to get a corpus of documents that then need involved human analysis to give the relevant sections. It seems that paragraph-level vectors are probably at the right conceptual level for refinement.
Ultimately I guess, what is considered a document is somewhat arbitrary. But I wondered if you'd looked at - or if someone here knows about - MLs for retrieval that consider documents at a mix of conceptual levels to improve retrieval. So, pairwise paragraph-level after a broader retrieval would be a simple example.
I guess for looking at CV/resumes that might relate to finding someone who was gardener at Google and then later used ML for graphic design, vs someone who did ML at Google ... which might be a similar document vector (poor example, but you get the picture).
Currently I'm seeing document level references to source material, snippets based on keywords, but not paragraph level referencing as you'd have for legal decisions.
ashwindharne
Cool stuff! We use a similar process internally to rerank and filter our cold outbound lists. We just use an off-the-shelf model as the judge, give it a custom criteria, and let it run until some set number of iterations. It's helped narrow down wide searches to the maximally relevant set of people (few thousand medium-bad matches to few hundred good matches)
It's not cheap and it's not fast, but it definitely works pretty well!
jayunit
Very interesting! What are some examples of criteria that you can evaluate pairwise, but couldn't score individually?
ashwindharne
It's all unstructured text (title, company, company size, experience, skills, raw text, etc.) and LLMs are pretty bad at assigning numerical scores in a vacuum. To make it work, we'd have to provide a representative set of examples, break scoring down by specific field, etc.
Kind of a lot of work compared to just dumping the text of 2 profiles into a context window along with a vague description of what I want, and having the LLM make the binary judgment.
bravura
Pairwise rank constraints involve fewer assumptions that per-item scoring about the underlying nature of the data, thus they are more robust.
npip99
Yeah that's exactly what we observed. Our goal was to create an absolute score that's completely independent from the Corpus, which is difficult because naturally all ELO distributions are inherently tied to the corpus itself!
When we were exploring the mathematical foundations, we considered ELO scoring against a "Universal Corpus" based on the natural entropy of human language (Obviously that's intractable, but sometimes this term cancels out like in the DPO proof).
But eventually we figured out a method using cross-query comparisons to assign an "ELO bias" to all document ELOs within a given query's candidate list. This normalizes it correctly such that when a candidate list is all bad, the ELOs shift low. And when the candidate list is all good, the ELOs shift high. Even when the relative ELOs are all the same.
Neywiny
I have a paper that got denied but it was about using 2AFC sorting to do this instead of elo. It has a defined end unlike elo scores. The code is on my github and focuses on humans sorting images but basically if you have a python sort function, you put your comparison as the key instead of assigning the comparison a numeric score. Then the algorithm does the rest
Code: https://github.com/Neywiny/merge-sort Conference/abstract presentation: https://www.spiedigitallibrary.org/conference-proceedings-of...
reactordev
I was going to mention this approach as well. The problem with the OP is that it has assumption bias and the entire chain is based on that assumption. It’s novel. But the original idea was to more evenly distribute scores so you can find real relevance and I think 2AFC is better. But I don’t have time to verify and post a paper about it.
npip99
Yes our pairwise method is based entirely on 2AFC comparisons, for both intra-query and inter-query ELO calculations.
It's definitely the best if not only way to get extremely high signal, and a score assignment that actually converges the more you sample.
In terms of the "F" in 2AFC, we actually have this amusing snippet from our prompt:
> Do NOT output a score of 0.0, ensure to focus on which document is superior, and provide a negative or positive float between -1.0 and 1.0.
Neywiny
It's probably because that's what we used, but nAFC has been my go-to since I first learned about it. Literally any time there's a ranking, even for dumb stuff like tier list videos on YouTube, they're too arbitrary. Ok you ranked this snack an 8/10. Based on what? And then they go back and say "actually I'm going to move that to a 7". AFC fixes all of that.
ghita_
would love to check out the code if you have it!
Neywiny
https://github.com/Neywiny/merge-sort
It was actually done to counter Elo based approaches so there's some references in the readme on how to prove who's better. I haven't run this code in 5 years and haven't developed on it in maybe 6, but I can probably fix any issues that come up. My co-author looks to have diverged a bit. Haven't checked out his code. https://github.com/FrankWSamuelson/merge-sort . There may also be a fork by the FDA itself, not sure. This work was done for the FDA's medical imaging device evaluation division
seanhunter
Fun fact about ELO. It's natural to think that it is some kind of initialism, but in fact ELO doesn't stand for anything. It's the name of the guy who invented the system. https://en.wikipedia.org/wiki/Arpad_Elo
So don't say it "E.L.O." (unless you're talking about the band, I guess), say "ee-low"
esafak
It should be Elo rating! https://en.wikipedia.org/wiki/Elo_rating_system
kayge
Thanks for this :) I had never heard of Elo until I noticed this morning that the new Chess course in Duolingo gives you an Elo ranking after a few rounds against Oscar. Probably would have skipped right over this story and comments otherwise, but now I have a fun bit of non-tech trivia to share if it ever comes up in small talk someday.
bbstats
(also because it's a name, you don't capitalize all three letters)
npip99
I often see it rendered as "Elo" but I've always found it more natural to capitalize as "ELO", but perhaps I should swap to "Elo" given this. Pronouncing "ee-low" is certainly the way it's done in chess/esports though!
reactordev
It’s also popular in ranking online players in games… really any game where there’s an win/loss ranking..
ghita_
oh interesting, had no idea, thanks for sharing
amelius
What was his ELO rating?
mkaszkowiak
Happy to see competition in rerankers! Good luck with your product.
My questions: what languages do your models currently support? Did you perform multilingual benchmarks? Couldn't find an answer on the website
ghita_
Thanks! We trained on most european languages (english, french, spanish, russian...), arabic, and chinese so it does well on those! We haven't tested too much on other languages, but happy to do so if there is a use case
etk934
Will the reranker trained with MSE be better calibrated than those trained with InfoNCE? Will threshold on reranker scores be more useful in RAG applications?
npip99
We tried a bradley-terry loss function, as calculated with https://hackmd.io/@-Gjw1zWMSH6lMPRlziQFEw/SJ8sRl1Zge
We found that MSE after elo-adjustment worked equally well. And, MSE lets you shuffle (q, d) across the dataset which has good statistical properties (Versus contrastive, which makes you sample the same query many times within a single minibatch)
In this case "InfoNCE" isn't applicable because the reranker's output is a scalar, not a vector. So that's why we checked both bradley-terry and MSE.
rahulnair23
Interesting work.
For a slightly different take using a similar intuition, see our paper [at ACL 2024](https://arxiv.org/abs/2402.14860) on ranking LLMs which may be of interest.
Our HuggingFace space has some examples: https://huggingface.co/spaces/ibm/llm-rank-themselves
ghita_
thank you, will check out the paper, the hf space is very cool!
yalok
What’s the expected additional latency due to running this re-ranker?
ghita_
It actually runs pretty fast, our benchmarks show ~149ms for 12665 bytes. It's faster than many other models
esafak
I would prominently display your benchmarks (against your competitors, of course). That's your selling point, right?
ghita_
Yes! We did this here: https://www.zeroentropy.dev/blog/announcing-zeroentropys-fir... We wanted to share the approach with the community in this post. It does do better than competitors though!
bbstats
Little reminder that Elo is a guy, not an acronym :)
esafak
I would have titled it "Improving ranking..."
I like that it works with `sentence_transformers`
dang
We could change the title to "Improving search ranking with chess Elo scores". Anybody object?
Edit: ok, done. Submitted title was "Show HN: Improving RAG with chess Elo scores".
ghita_
yes we found it hard to find a good title for this, thanks for the feedback
sippeangelo
Really cool stuff! Just want to let you know you forgot to link to the evals at the end.
ghita_
oh waw thanks for flagging, just fixed, thanks!
Hello HN,
I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents.
We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0.
We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps: * Collect soft preferences between pairs of documents using an ensemble of LLMs. * Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into absolute per-document scores. * Normalize relevance scores across queries using a bias correction step, modeled using cross-query comparisons and solved with MLE.
You can try the models either through our API (https://docs.zeroentropy.dev/models), or via HuggingFace (https://huggingface.co/zeroentropy/zerank-1-small).
We would love this community's feedback on the models, and the training approach. A full technical report is also going to be released soon.
Thank you!