Show HN: Improving search ranking with chess Elo scores
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!
The link in the article to the full blog explaining rerankers is 404ing for me.
Questions to you as an expert related to search ranking. With o3 and source quality thresholds when performing web search. Could we implement an ELO-style cutoff where systems default to “I don’t know” rather than citing low-ranked sources?
Currently o3’s main weakness is mixing high-quality sources with poor ones when it uses the web search in the same response. The answer sounds authoritative throughout, but parts are backed by unreliable sources. This makes it harder to trust even the well-sourced portions (e.g. believing the US election is next year - not a hallucination but a poorly date formatted source it used). It also makes the response a lot slower.
Would a hard quality threshold be better than the current approach of seamlessly blending good and bad sources?
https://github.com/pfmonville/whole_history_rating
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.
LambdaMART's approach seems better in that respect.
https://medium.com/@nikhilbd/pointwise-vs-pairwise-vs-listwi...
It's not cheap and it's not fast, but it definitely works pretty well!
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.
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.
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.
Code: https://github.com/Neywiny/merge-sort Conference/abstract presentation: https://www.spiedigitallibrary.org/conference-proceedings-of...
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
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.
So don't say it "E.L.O." (unless you're talking about the band, I guess), say "ee-low"
https://en.wikipedia.org/wiki/Corrado_Gini
2065
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.
My questions: what languages do your models currently support? Did you perform multilingual benchmarks? Couldn't find an answer on the website
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
I like that it works with `sentence_transformers`
Edit: ok, done. Submitted title was "Show HN: Improving RAG with chess Elo scores".