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<div class="quarto-title-meta-heading">Published</div>
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<p class="date">2023-10-29</p>
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<br>
🪤
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</div><section id="sparse-autoencoder" class="level1">
<h1>Sparse Autoencoder</h1>
<section id="what-i-thought-before-reading-anything" class="level2">
<h2 class="anchored" data-anchor-id="what-i-thought-before-reading-anything">What I thought before reading anything</h2>
<p>Autoencoding is about having a neural network find an alternate representation of the data by having it search for a set of weights that can convert the data into the alternate representation and also be able to reconstruct the original version from that alternate representation.</p>
<p>Usually the alternate version is smaller than the original, so autoencoding is about compression.</p>
<p>But, in case of network interpretability, the alternate version is larger than the original.<br>
By having a larger alternate, anthropic hypothesizes, the polysemanticity of the activations from the original get disentangled.</p>
<p>Now what I don’t get is:<br>
- Why does this kind of autoencoding do <em>anything</em>?</p>
</section>
<section id="checking-cunningham-et-al-sparse-autoencoders-find-highly-interpretable-features-in-language-models" class="level2">
<h2 class="anchored" data-anchor-id="checking-cunningham-et-al-sparse-autoencoders-find-highly-interpretable-features-in-language-models">Checking Cunningham et al “Sparse Autoencoders Find Highly Interpretable Features in Language Models”</h2>
<p>Apparently, <a href="https://hyp.is/4Aw9QHY5Ee6MbHtykTIpYQ/arxiv.org/pdf/2309.08600.pdf">a language model can better predict the output of a target language model when the predictor gets access to a description of the feature</a>.</p>
<p>I agree this <em>hints</em> at the inherent interpretability of that feature, but this is <em>not</em> clean.<br>
So what would I want out of <a href="./xai.html">interpretability</a>? …</p>
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instance.show();
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instance.show();
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parent.appendChild(div);
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window.addEventListener(
"resize",
throttle(() => {
elRect = undefined;
if (selectedAnnoteEl) {
selectCodeLines(selectedAnnoteEl);
}
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);
function throttle(fn, ms) {
let throttle = false;
let timer;
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fn.apply(this, args);
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} 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');
}
});
}
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const cites = parentEl.dataset.cites;
if (cites) {
return {
el,
cites: cites.split(' ')
};
} else {
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}
} 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);
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popup.appendChild(citeDiv);
});
return popup.innerHTML;
});
}
}
});
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