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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>
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  <div class="description">
    A simpler yes/no classification setup improves determinism, scales better on larger batches, and preserves evaluation quality.
  </div>
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    <div class="quarto-title-meta-heading">Author</div>
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             <p>Radoslav Ralev </p>
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    <div class="quarto-title-meta-heading">Published</div>
    <div class="quarto-title-meta-contents">
      <p class="date">March 24, 2026</p>
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<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) &gt; 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 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) {
              //compute the position of the single el (top and bottom and make a div)
              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) {
              // cook up a div (if necessary) and position it 
              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;
        };
          // Handle positioning of the toggle
      window.addEventListener(
        "resize",
        throttle(() => {
          elRect = undefined;
          if (selectedAnnoteEl) {
            selectCodeLines(selectedAnnoteEl);
          }
        }, 10)
      );
      function throttle(fn, ms) {
      let throttle = false;
      let timer;
        return (...args) => {
          if(!throttle) { // first call gets through
              fn.apply(this, args);
              throttle = true;
          } else { // all the others get throttled
              if(timer) clearTimeout(timer); // cancel #2
              timer = setTimeout(() => {
                fn.apply(this, args);
                timer = throttle = false;
              }, ms);
          }
        };
      }
        // Attach click handler to the DT
        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 {
              // Unselect the line
              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>
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