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| <li><a href="#the-new-classification-setup" id="toc-the-new-classification-setup" class="nav-link" data-scroll-target="#the-new-classification-setup">The new classification setup</a></li> |
| <li><a href="#what-improved" id="toc-what-improved" class="nav-link" data-scroll-target="#what-improved">What improved</a></li> |
| <li><a href="#prompt-comparison-and-implementation-behavior" id="toc-prompt-comparison-and-implementation-behavior" class="nav-link" data-scroll-target="#prompt-comparison-and-implementation-behavior">Prompt comparison and implementation behavior</a></li> |
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| <li><a href="#benchmark-observations" id="toc-benchmark-observations" class="nav-link" data-scroll-target="#benchmark-observations">Benchmark observations</a></li> |
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| <h1 class="title">An Alternative LLM-as-a-Judge Local Pipeline for Better Stability and Batch Scaling</h1> |
| <div class="quarto-categories"> |
| <div class="quarto-category">evaluation</div> |
| <div class="quarto-category">llm</div> |
| <div class="quarto-category">vllm</div> |
| <div class="quarto-category">benchmarking</div> |
| </div> |
| </div> |
|
|
| <div> |
| <div class="description"> |
| A simpler yes/no classification setup improves determinism, scales better on larger batches, and preserves evaluation quality. |
| </div> |
| </div> |
|
|
|
|
| <div class="quarto-title-meta"> |
|
|
| <div> |
| <div class="quarto-title-meta-heading">Author</div> |
| <div class="quarto-title-meta-contents"> |
| <p>Radoslav Ralev </p> |
| </div> |
| </div> |
| |
| <div> |
| <div class="quarto-title-meta-heading">Published</div> |
| <div class="quarto-title-meta-contents"> |
| <p class="date">March 24, 2026</p> |
| </div> |
| </div> |
| |
| |
| </div> |
| |
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| </header> |
|
|
|
|
| <p>This post summarizes an internal evaluation of a revised local LLM-as-a-judge pipeline for cache-hit classification.</p> |
| <p>The main change is simple: instead of asking the model to generate a structured JSON object for each example, the pipeline now asks for a binary decision, <code>yes</code> or <code>no</code>, and compares the token probabilities <code>p(yes)</code> and <code>p(no)</code> at inference time.</p> |
| <p>That small interface change has a large systems impact. It removes fragile output parsing, reduces run failures, improves determinism across batch orderings, and makes larger-batch execution much more practical.</p> |
| <section id="why-the-earlier-approach-was-brittle" class="level2"> |
| <h2 class="anchored" data-anchor-id="why-the-earlier-approach-was-brittle">Why the earlier approach was brittle</h2> |
| <p>The older implementation had three main weaknesses:</p> |
| <ul> |
| <li>It relied on the model to emit valid JSON, so small formatting errors could break a run.</li> |
| <li>It showed non-deterministic behavior across batched inference, where identical sentence pairs could receive different labels depending on batch composition.</li> |
| <li>It was effectively zero-shot, which limited performance compared with a prompt design that better framed the classification task.</li> |
| </ul> |
| <p>In practice, this meant that the evaluation pipeline was doing more work than necessary. The model was spending capacity on output formatting instead of the classification decision itself.</p> |
| </section> |
| <section id="the-new-classification-setup" class="level2"> |
| <h2 class="anchored" data-anchor-id="the-new-classification-setup">The new classification setup</h2> |
| <p>The revised pipeline narrows the task to a direct binary choice:</p> |
| <ul> |
| <li>Predict <code>yes</code> or <code>no</code></li> |
| <li>Compare <code>p(yes)</code> against <code>p(no)</code></li> |
| <li>Mark the pair as a cache hit when <code>p(yes) > p(no)</code></li> |
| </ul> |
| <p>This design makes the system easier to reason about and easier to scale. The surrounding code owns the output structure, while the model focuses on the decision boundary.</p> |
| </section> |
| <section id="what-improved" class="level2"> |
| <h2 class="anchored" data-anchor-id="what-improved">What improved</h2> |
| <p>The updated pipeline delivers four practical gains:</p> |
| <ul> |
| <li>Improved stability through fewer failed runs</li> |
| <li>Better batch-size scaling on L40S GPUs</li> |
| <li>Deterministic outputs for repeated sentence pairs</li> |
| <li>Lower latency from a simpler forward-pass pattern</li> |
| </ul> |
| <p>These are not just implementation conveniences. They matter directly for evaluation throughput, reproducibility, and confidence in benchmark results.</p> |
| </section> |
| <section id="prompt-comparison-and-implementation-behavior" class="level2"> |
| <h2 class="anchored" data-anchor-id="prompt-comparison-and-implementation-behavior">Prompt comparison and implementation behavior</h2> |
| <p>The writeup compares the old and new setups across different batch sizes and prompt choices.</p> |
| <p>One key takeaway is that the new implementation produces more stable metrics with less variance. The goal is not to claim a dramatic quality jump from prompt engineering alone, but to show that the revised setup behaves more consistently under scale.</p> |
| <p>The experiments also compare Hugging Face execution with <code>vLLM</code>. Metric quality stays comparable, while <code>vLLM</code> provides a meaningful speed advantage in most runs.</p> |
| </section> |
| <section id="sub-10b-model-benchmark-on-quora-question-pairs" class="level2"> |
| <h2 class="anchored" data-anchor-id="sub-10b-model-benchmark-on-quora-question-pairs">Sub-10B model benchmark on Quora Question Pairs</h2> |
| <p>The second part of the evaluation benchmarks local models under 10B parameters on Quora Question Pairs.</p> |
| <p>Two experimental choices make the results more trustworthy:</p> |
| <ul> |
| <li>The evaluation sample size increases from <code>1024</code> to <code>4096</code></li> |
| <li>Each model is run <code>5</code> times on separate 4096-sample batches</li> |
| </ul> |
| <p>That setup makes it possible to report means and standard deviations for precision, recall, F1, and runtime, rather than relying on a single noisy run.</p> |
| </section> |
| <section id="benchmark-observations" class="level2"> |
| <h2 class="anchored" data-anchor-id="benchmark-observations">Benchmark observations</h2> |
| <p>The main observations from the benchmark are:</p> |
| <ul> |
| <li>Precision, recall, and F1 remain stable across repeated runs</li> |
| <li>Top-performing small models show low variance, suggesting the results are not driven by one favorable sample</li> |
| <li><code>vLLM</code> is often about <code>2x</code> faster than the Hugging Face path, though some smaller models can occasionally be faster with HF because of overhead effects</li> |
| </ul> |
| <p>The overall message is that local LLM judging can be both practical and reproducible when the task formulation is kept narrow and the serving path is optimized.</p> |
| </section> |
| <section id="takeaway" class="level2"> |
| <h2 class="anchored" data-anchor-id="takeaway">Takeaway</h2> |
| <p>The strongest result here is not a single benchmark number. It is the systems lesson.</p> |
| <p>When an evaluation pipeline asks a model to do only the minimum necessary work, the entire stack becomes easier to scale and more reliable. In this case, replacing structured generation with probability-based binary classification improves stability, preserves evaluation quality, and makes local LLM judging a stronger option for large batch workloads.</p> |
|
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| const note = window.document.getElementById(id); |
| if (note !== null) { |
| try { |
| const html = processXRef(id, note.cloneNode(true)); |
| instance.setContent(html); |
| } finally { |
| instance.enable(); |
| instance.show(); |
| } |
| } else { |
| |
| fetch(url.split('#')[0]) |
| .then(res => res.text()) |
| .then(html => { |
| const parser = new DOMParser(); |
| const htmlDoc = parser.parseFromString(html, "text/html"); |
| const note = htmlDoc.getElementById(id); |
| if (note !== null) { |
| const html = processXRef(id, note); |
| instance.setContent(html); |
| } |
| }).finally(() => { |
| instance.enable(); |
| instance.show(); |
| }); |
| } |
| } else { |
| |
| |
| fetch(url) |
| .then(res => res.text()) |
| .then(html => { |
| const parser = new DOMParser(); |
| const htmlDoc = parser.parseFromString(html, "text/html"); |
| const note = htmlDoc.querySelector('main.content'); |
| if (note !== null) { |
| |
| |
| |
| if (note.children.length > 0 && note.children[0].tagName === "HEADER") { |
| note.children[0].remove(); |
| } |
| const html = processXRef(null, note); |
| instance.setContent(html); |
| } |
| }).finally(() => { |
| instance.enable(); |
| instance.show(); |
| }); |
| } |
| }, function(instance) { |
| }); |
| } |
| let selectedAnnoteEl; |
| const selectorForAnnotation = ( cell, annotation) => { |
| let cellAttr = 'data-code-cell="' + cell + '"'; |
| let lineAttr = 'data-code-annotation="' + annotation + '"'; |
| const selector = 'span[' + cellAttr + '][' + lineAttr + ']'; |
| return selector; |
| } |
| const selectCodeLines = (annoteEl) => { |
| const doc = window.document; |
| const targetCell = annoteEl.getAttribute("data-target-cell"); |
| const targetAnnotation = annoteEl.getAttribute("data-target-annotation"); |
| const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation)); |
| const lines = annoteSpan.getAttribute("data-code-lines").split(","); |
| const lineIds = lines.map((line) => { |
| return targetCell + "-" + line; |
| }) |
| let top = null; |
| let height = null; |
| let parent = null; |
| if (lineIds.length > 0) { |
| |
| const el = window.document.getElementById(lineIds[0]); |
| top = el.offsetTop; |
| height = el.offsetHeight; |
| parent = el.parentElement.parentElement; |
| if (lineIds.length > 1) { |
| const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]); |
| const bottom = lastEl.offsetTop + lastEl.offsetHeight; |
| height = bottom - top; |
| } |
| if (top !== null && height !== null && parent !== null) { |
| |
| let div = window.document.getElementById("code-annotation-line-highlight"); |
| if (div === null) { |
| div = window.document.createElement("div"); |
| div.setAttribute("id", "code-annotation-line-highlight"); |
| div.style.position = 'absolute'; |
| parent.appendChild(div); |
| } |
| div.style.top = top - 2 + "px"; |
| div.style.height = height + 4 + "px"; |
| div.style.left = 0; |
| let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); |
| if (gutterDiv === null) { |
| gutterDiv = window.document.createElement("div"); |
| gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter"); |
| gutterDiv.style.position = 'absolute'; |
| const codeCell = window.document.getElementById(targetCell); |
| const gutter = codeCell.querySelector('.code-annotation-gutter'); |
| gutter.appendChild(gutterDiv); |
| } |
| gutterDiv.style.top = top - 2 + "px"; |
| gutterDiv.style.height = height + 4 + "px"; |
| } |
| selectedAnnoteEl = annoteEl; |
| } |
| }; |
| const unselectCodeLines = () => { |
| const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"]; |
| elementsIds.forEach((elId) => { |
| const div = window.document.getElementById(elId); |
| if (div) { |
| div.remove(); |
| } |
| }); |
| selectedAnnoteEl = undefined; |
| }; |
| |
| window.addEventListener( |
| "resize", |
| throttle(() => { |
| elRect = undefined; |
| if (selectedAnnoteEl) { |
| selectCodeLines(selectedAnnoteEl); |
| } |
| }, 10) |
| ); |
| function throttle(fn, ms) { |
| let throttle = false; |
| let timer; |
| return (...args) => { |
| if(!throttle) { |
| fn.apply(this, args); |
| throttle = true; |
| } else { |
| if(timer) clearTimeout(timer); |
| timer = setTimeout(() => { |
| fn.apply(this, args); |
| timer = throttle = false; |
| }, ms); |
| } |
| }; |
| } |
| |
| const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); |
| for (const annoteDlNode of annoteDls) { |
| annoteDlNode.addEventListener('click', (event) => { |
| const clickedEl = event.target; |
| if (clickedEl !== selectedAnnoteEl) { |
| unselectCodeLines(); |
| const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active'); |
| if (activeEl) { |
| activeEl.classList.remove('code-annotation-active'); |
| } |
| selectCodeLines(clickedEl); |
| clickedEl.classList.add('code-annotation-active'); |
| } else { |
| |
| unselectCodeLines(); |
| clickedEl.classList.remove('code-annotation-active'); |
| } |
| }); |
| } |
| const findCites = (el) => { |
| const parentEl = el.parentElement; |
| if (parentEl) { |
| const cites = parentEl.dataset.cites; |
| if (cites) { |
| return { |
| el, |
| cites: cites.split(' ') |
| }; |
| } else { |
| return findCites(el.parentElement) |
| } |
| } else { |
| return undefined; |
| } |
| }; |
| var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]'); |
| for (var i=0; i<bibliorefs.length; i++) { |
| const ref = bibliorefs[i]; |
| const citeInfo = findCites(ref); |
| if (citeInfo) { |
| tippyHover(citeInfo.el, function() { |
| var popup = window.document.createElement('div'); |
| citeInfo.cites.forEach(function(cite) { |
| var citeDiv = window.document.createElement('div'); |
| citeDiv.classList.add('hanging-indent'); |
| citeDiv.classList.add('csl-entry'); |
| var biblioDiv = window.document.getElementById('ref-' + cite); |
| if (biblioDiv) { |
| citeDiv.innerHTML = biblioDiv.innerHTML; |
| } |
| popup.appendChild(citeDiv); |
| }); |
| return popup.innerHTML; |
| }); |
| } |
| } |
| }); |
| </script> |
| </div> |
| <footer class="footer"> |
| <div class="nav-footer"> |
| <div class="nav-footer-left"> |
| <p>Discover this project on Hugging Face Spaces: https://huggingface.co/spaces/srijithrajamohan/ai-research-redis</p> |
| </div> |
| <div class="nav-footer-center"> |
| |
| </div> |
| <div class="nav-footer-right"> |
| |
| </div> |
| </div> |
| </footer> |
|
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