Найден корректный robots.txt
Файл управления сканированием сайта роботами
?
Robots.txt указывает поисковым роботам, какие страницы сканировать, а какие — нет. Ошибки в файле могут случайно закрыть важные разделы от индексации.
Кол-во редиректов для файла robots.txt 3 слишком большое! Это может вызывать затруднение при индексации поисковыми роботами!
Проверяемая страница не запрещена в robots.txt.
Цепочка редиректов для файла robots.txt:
http://questdecoding.com/robots.txt
301 MovedPermanently
https://questdecoding.com/robots.txt
301 MovedPermanently
https://www.questdecoding.com/robots.txt
200 OK
Показать содержимое robots.txt
<!DOCTYPE html>
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<title>QUEST</title>
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<div class="container">
<div class="title">
<span class="purple">QUEST</span>: Quality-Aware Metropolis-Hastings Sampling for <span class="purple">Machine Translation</span>
</div>
<div class="neurips-badge">NEURIPS2024</div>
<div class="team">
<a href="https://www.goncalofaria.com">
<div class=" team-member">
<img src="assets/goncalo.png" alt="Gonçalo Faria">
<p>Gonçalo* Faria</p>
</div>
</a>
<a href="https://sweta20.github.io/">
<div class=" team-member">
<img src="assets/sweta.jpg" alt="Sweta Agrawal">
<p>Sweta Agrawal</p>
</div>
</a>
<a href="https://antonio-farinhas.github.io/">
<div class=" team-member ">
<img src="assets/antonio.jpg" alt="Antonio Farinhas">
<p>Antonio Farinhas</p>
</div>
</a>
<a href="https://scholar.google.com/citations?user=20ApDosAAAAJ&hl=en">
<div class=" team-member">
<img src="assets/jose.jpg" alt="Jose Souza">
<p>Jose Souza</p>
</div>
</a>
<a href="https://ricardorei.github.io/">
<div class=" team-member">
<img src="assets/ricardo.jpg" alt="Ricardo Rei">
<p>Ricardo Rei</p>
</div>
</a>
<a href="https://andre-martins.github.io/">
<div class=" team-member ">
<img src="assets/andre.jpg" alt="André Martins">
<p>André Martins</p>
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<a href="https://arxiv.org/abs/2406.00049">
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Code
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<a href="mailto:gfaria@cs.washington.edu?subject=Paper Inquiry&body=I have some questions about QUEST!">
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Contact
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<em>
<strong>TL;DR:</strong> <i style="font-weight: 400;">This paper presents a method to generate diverse and high-quality machine translations by sampling from a Gibbs distribution using the Metropolis-Hastings algorithm.</i>
</em>
</div>
<div class="abstract">
<h2><strong>Abstract:</strong></h2>
<p lang="en">An important challenge in machine translation (MT) is to generate high-quality and diverse translations. Prior work has shown that the estimated likelihood from the MT model correlates poorly with translation quality. In contrast, quality evaluation metrics (such as COMET or BLEURT) exhibit high correlations with human judgments, which has motivated their use as rerankers (such as quality-aware and minimum Bayes risk decoding). However, relying on a single translation with high estimated quality increases the chances of "gaming the metric''. In this paper, we address the problem of sampling a set of high-quality and diverse translations. We provide a simple and effective way to avoid over-reliance on noisy quality estimates by using them as the energy function of a Gibbs distribution. Instead of looking for a mode in the distribution, we generate multiple samples from high-density areas through the Metropolis-Hastings algorithm, a simple Markov chain Monte Carlo approach. The results show that our proposed method leads to high-quality and diverse outputs across multiple language pairs (English -> {German, Russian}) with two strong decoder-only LLMs (Alma-7b, Tower-7b).
</p>
</div>
<iframe src="mcmc-sequence-animation.html" style="width: 100%; height: 500px; border: none;" frameborder="0"></iframe>
<div class="bib-section">
<div class="bibtitle">BibTeX</h2>
<pre><code>@misc{faria2024questqualityawaremetropolishastingssampling,
title={QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation},
author={Gonçalo R. A. Faria and Sweta Agrawal and António Farinhas and Ricardo Rei and José G. C. de Souza and André F. T. Martins},
year={2024},
eprint={2406.00049},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.00049},
}</code></pre>
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<h2>Follow-up Work</h2>
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<h3><span class="purple">Sample</span>, Don't Search: <br> Rethinking <span class="purple">Test-Time Alignment</span> for Language Models
</span></h3>
<p>QAlign a new test-time alignment approach that improves language model performance by using Markov chain Monte Carlo methods. </p>
<a href="https://arxiv.org/pdf/2504.03790" class="card-link">Paper</a>
<a href="rlhf.html" class="card-link">Website</a>
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