<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.10.0">Jekyll</generator><link href="https://proceedings.mlr.press/v262/feed.xml" rel="self" type="application/atom+xml" /><link href="https://proceedings.mlr.press/v262/" rel="alternate" type="text/html" /><updated>2025-01-27T08:06:06+00:00</updated><id>https://proceedings.mlr.press/v262/feed.xml</id><title type="html">Proceedings of Machine Learning Research</title><subtitle>Proceedings of The 4th NeurIPS Efficient Natural Language and Speech Processing Workshop
  Held in Vancouver, British Columbia, Canada on 14 December 2024

Published as Volume 262 by the Proceedings of Machine Learning Research on 10 December 2024.

Volume Edited by:
  Mehdi Rezagholizadeh
  Peyman Passban
  Soheila Samiee
  Vahid Partovi Nia
  Yu Cheng
  Yue Deng
  Qun Liu
  Boxing Chen

Series Editors:
  Neil D. Lawrence
</subtitle><author><name>PMLR</name></author><entry><title type="html">AdaEDL: Early Draft Stopping for Speculative Decoding of Large Language Models via an Entropy-based Lower Bound on Token Acceptance Probability</title><link href="https://proceedings.mlr.press/v262/agrawal24a.html" rel="alternate" type="text/html" title="AdaEDL: Early Draft Stopping for Speculative Decoding of Large Language Models via an Entropy-based Lower Bound on Token Acceptance Probability" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/agrawal24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/agrawal24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Sudhanshu&quot;, &quot;family&quot;=&gt;&quot;Agrawal&quot;}, {&quot;given&quot;=&gt;&quot;Wonseok&quot;, &quot;family&quot;=&gt;&quot;Jeon&quot;}, {&quot;given&quot;=&gt;&quot;Mingu&quot;, &quot;family&quot;=&gt;&quot;Lee&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">SuperPos-Prompt: Enhancing Soft Prompt Tuning of Language Models with Superposition of Multi Token Embeddings</title><link href="https://proceedings.mlr.press/v262/ali-sadraei-javaheri24a.html" rel="alternate" type="text/html" title="SuperPos-Prompt: Enhancing Soft Prompt Tuning of Language Models with Superposition of Multi Token Embeddings" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/ali-sadraei-javaheri24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/ali-sadraei-javaheri24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Mohammad&quot;, &quot;family&quot;=&gt;&quot;Ali Sadraei Javaheri&quot;}, {&quot;given&quot;=&gt;&quot;Ehsaneddin&quot;, &quot;family&quot;=&gt;&quot;Asgari&quot;}, {&quot;given&quot;=&gt;&quot;Alice&quot;, &quot;family&quot;=&gt;&quot;C. McHardy&quot;}, {&quot;given&quot;=&gt;&quot;Hamid&quot;, &quot;family&quot;=&gt;&quot;R. Rabiee&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models</title><link href="https://proceedings.mlr.press/v262/alizadeh-vahid24a.html" rel="alternate" type="text/html" title="Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/alizadeh-vahid24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/alizadeh-vahid24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Keivan&quot;, &quot;family&quot;=&gt;&quot;Alizadeh-Vahid&quot;}, {&quot;given&quot;=&gt;&quot;Seyed&quot;, &quot;family&quot;=&gt;&quot;Iman Mirzadeh&quot;}, {&quot;given&quot;=&gt;&quot;Hooman&quot;, &quot;family&quot;=&gt;&quot;Shahrkokhi&quot;}, {&quot;given&quot;=&gt;&quot;Dmitry&quot;, &quot;family&quot;=&gt;&quot;Belenko&quot;}, {&quot;given&quot;=&gt;&quot;Frank&quot;, &quot;family&quot;=&gt;&quot;Sun&quot;}, {&quot;given&quot;=&gt;&quot;Minsik&quot;, &quot;family&quot;=&gt;&quot;Cho&quot;}, {&quot;given&quot;=&gt;&quot;Mohammad&quot;, &quot;family&quot;=&gt;&quot;Hossein Sekhavat&quot;}, {&quot;given&quot;=&gt;&quot;Moin&quot;, &quot;family&quot;=&gt;&quot;Nabi&quot;}, {&quot;given&quot;=&gt;&quot;Mehrdad&quot;, &quot;family&quot;=&gt;&quot;Farajtabar&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Text Summarization With Graph Attention Networks</title><link href="https://proceedings.mlr.press/v262/ardestani24a.html" rel="alternate" type="text/html" title="Text Summarization With Graph Attention Networks" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/ardestani24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/ardestani24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Mohammadreza&quot;, &quot;family&quot;=&gt;&quot;Ardestani&quot;}, {&quot;given&quot;=&gt;&quot;Yllias&quot;, &quot;family&quot;=&gt;&quot;Chali&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Computational Bottlenecks of Training Small-scale Large Language Models</title><link href="https://proceedings.mlr.press/v262/ashkboos24a.html" rel="alternate" type="text/html" title="Computational Bottlenecks of Training Small-scale Large Language Models" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/ashkboos24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/ashkboos24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Saleh&quot;, &quot;family&quot;=&gt;&quot;Ashkboos&quot;}, {&quot;given&quot;=&gt;&quot;Seyed&quot;, &quot;family&quot;=&gt;&quot;Iman Mirzadeh&quot;}, {&quot;given&quot;=&gt;&quot;Keivan&quot;, &quot;family&quot;=&gt;&quot;Alizadeh-Vahid&quot;}, {&quot;given&quot;=&gt;&quot;Mohammad&quot;, &quot;family&quot;=&gt;&quot;Hossein Sekhavat&quot;}, {&quot;given&quot;=&gt;&quot;Moin&quot;, &quot;family&quot;=&gt;&quot;Nabi&quot;}, {&quot;given&quot;=&gt;&quot;Mehrdad&quot;, &quot;family&quot;=&gt;&quot;Farajtabar&quot;}, {&quot;given&quot;=&gt;&quot;Fartash&quot;, &quot;family&quot;=&gt;&quot;Faghri&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation</title><link href="https://proceedings.mlr.press/v262/azimi24a.html" rel="alternate" type="text/html" title="KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/azimi24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/azimi24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Rambod&quot;, &quot;family&quot;=&gt;&quot;Azimi&quot;}, {&quot;given&quot;=&gt;&quot;Rishav&quot;, &quot;family&quot;=&gt;&quot;Rishav&quot;}, {&quot;given&quot;=&gt;&quot;Marek&quot;, &quot;family&quot;=&gt;&quot;Teichmann&quot;}, {&quot;given&quot;=&gt;&quot;Samira&quot;, &quot;family&quot;=&gt;&quot;Ebrahimi Kahou&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Speculative Streaming: Fast LLM Inference without Auxiliary Models</title><link href="https://proceedings.mlr.press/v262/bhendawade24a.html" rel="alternate" type="text/html" title="Speculative Streaming: Fast LLM Inference without Auxiliary Models" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/bhendawade24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/bhendawade24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Nikhil&quot;, &quot;family&quot;=&gt;&quot;Bhendawade&quot;}, {&quot;given&quot;=&gt;&quot;Irina&quot;, &quot;family&quot;=&gt;&quot;Belousova&quot;}, {&quot;given&quot;=&gt;&quot;Qichen&quot;, &quot;family&quot;=&gt;&quot;Fu&quot;}, {&quot;given&quot;=&gt;&quot;Henry&quot;, &quot;family&quot;=&gt;&quot;Mason&quot;}, {&quot;given&quot;=&gt;&quot;Mohammad&quot;, &quot;family&quot;=&gt;&quot;Rastegari&quot;}, {&quot;given&quot;=&gt;&quot;Mahyar&quot;, &quot;family&quot;=&gt;&quot;Najibi&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion Parameters</title><link href="https://proceedings.mlr.press/v262/chen24a.html" rel="alternate" type="text/html" title="OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion Parameters" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/chen24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/chen24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Zexin&quot;, &quot;family&quot;=&gt;&quot;Chen&quot;}, {&quot;given&quot;=&gt;&quot;Chengxi&quot;, &quot;family&quot;=&gt;&quot;Li&quot;}, {&quot;given&quot;=&gt;&quot;Xiangyu&quot;, &quot;family&quot;=&gt;&quot;Xie&quot;}, {&quot;given&quot;=&gt;&quot;Parijat&quot;, &quot;family&quot;=&gt;&quot;Dube&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts</title><link href="https://proceedings.mlr.press/v262/chung24a.html" rel="alternate" type="text/html" title="Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/chung24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/chung24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Youngseog&quot;, &quot;family&quot;=&gt;&quot;Chung&quot;}, {&quot;given&quot;=&gt;&quot;Dhruv&quot;, &quot;family&quot;=&gt;&quot;Malik&quot;}, {&quot;given&quot;=&gt;&quot;Jeff&quot;, &quot;family&quot;=&gt;&quot;Schneider&quot;}, {&quot;given&quot;=&gt;&quot;Yuanzhi&quot;, &quot;family&quot;=&gt;&quot;Li&quot;}, {&quot;given&quot;=&gt;&quot;Aarti&quot;, &quot;family&quot;=&gt;&quot;Singh&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Mai Ho‘omāuna i ka ‘Ai: Language Models Improve Automatic Speech Recognition in Hawaiian</title><link href="https://proceedings.mlr.press/v262/d-chaparala24a.html" rel="alternate" type="text/html" title="Mai Ho‘omāuna i ka ‘Ai: Language Models Improve Automatic Speech Recognition in Hawaiian" /><published>2024-12-10T00:00:00+00:00</published><updated>2024-12-10T00:00:00+00:00</updated><id>https://proceedings.mlr.press/v262/d-chaparala24a</id><content type="html" xml:base="https://proceedings.mlr.press/v262/d-chaparala24a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Kaavya&quot;, &quot;family&quot;=&gt;&quot;D Chaparala&quot;}, {&quot;given&quot;=&gt;&quot;Guido&quot;, &quot;family&quot;=&gt;&quot;Zarrella&quot;}, {&quot;given&quot;=&gt;&quot;Bruce&quot;, &quot;family&quot;=&gt;&quot;Torres Fischer&quot;}, {&quot;given&quot;=&gt;&quot;Larry&quot;, &quot;family&quot;=&gt;&quot;Kimura&quot;}, {&quot;given&quot;=&gt;&quot;Oiwi&quot;, &quot;family&quot;=&gt;&quot;Parker Jones&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry></feed>