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A quick guide to Amazon's papers at NeurIPS 2024

While large language models and other foundation models are well represented, traditional Amazon interests such as bandit problems and new topics such as AI for automated reasoning also get their due.

By Staff writer
December 10, 2024
3 min read
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The 2024 Conference on Neural Information Processing Systems (NeurIPS) — the premier conference in the field of AI — begins today, and the Amazon papers accepted there display the breadth of the company’s AI research.

Large language models (LLMs) and other foundation models have dominated the field for the past few years, and Amazon’s papers reflect that trend, covering topics such as retrieval-augmented generation, the use of LLMs for code generation, commonsense reasoning, and multimodal models. Training methodology also emerges as an area of focus, with papers on memory-efficient training, reinforcement learning with human feedback, classification with rejection, and convergence rates in transformer models.

But Amazon’s papers also demonstrate an abiding interest in topics such as bandit problems — long a staple of Amazon’s NeurIPS submissions — and speech processing, as well as newer concerns such as the applications of machine learning to scientific computing and automated reasoning. And one paper, “B’MOJO: Hybrid state space realizations of foundation models with eidetic and fading memory”, proposes a new paradigm of machine learning, rooted in the concept of transductive learning.

Automated reasoning

Neural model checking
Mirco Giacobbe, Daniel Kroening, Abhinandan Pal, Michael Tautschnig

Bandit problems

Adaptive experimentation when you can’t experiment
Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain

Online posterior sampling with a diffusion prior
Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song

Code generation

Training LLMs to better self-debug and explain code
Nan Jiang, Xiaopeng LI, Shiqi Wang, Qiang Zhou, Baishakhi Ray, Varun Kumar, Xiaofei Ma, Anoop Deoras

The LEDEX framework
The data collection and model-training framework proposed in "Training LLMs to better self-debug and explain code".

Commonsense reasoning

Can language models learn to skip steps?
Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Jiayang Cheng, Yue Zhang, Xipeng Qiu, Zheng Zhang

Computational fluid dynamics

WindsorML: High-fidelity computational fluid dynamics dataset for automotive aerodynamics
Neil Ashton, Jordan B. Angel, Aditya S. Ghate, Gaetan K. W. Kenway, Man Long Wong, Cetin Kiris, Astrid Walle, Danielle Maddix Robinson, Gary Page

LLM evaluation

SetLexSem Challenge: Using set operations to evaluate the lexical and semantic robustness of language models
Bardiya Akhbari, Manish Gawali, Nicholas Dronen

SetLexSem Challenge figure
To evaluate LLMs' robustness to semantic variation in set members, Amazon researchers and their colleagues created “deceptive” sets by sampling pairs of hypernyms (e.g., “mammal” and “vehicle”) and, from them, extracting hyponyms under three different conditions: (1) with the hyponyms as sampled; (2) with half of the set members swapped; and (3) with random sampling. LLMs exhibit a unique failure mode under the second condition (swapped), and the mean and variance in accuracy of the first condition (not swapped) are better than in the random baseline. This figure can be found in "SetLexSem Challenge: Using set operations to evaluate the lexical and semantic robustness of language models".

Memory management

Online weighted paging with unknown weights
Orin Levy, Aviv Rosenberg, Noam Touitou

Model architecture

B’MOJO: Hybrid state space realizations of foundation models with eidetic and fading memory
Luca Zancato, Arjun Seshadri, Yonatan Dukler, Aditya Golatkar, Yantao Shen, Ben Bowman, Matthew Trager, Alessandro Achille, Stefano Soatto

Privacy

Pre-training differentially private models with limited public data
Zhiqi Bu, Xinwei Zhang, Sheng Zha, Mingyi Hong

Reconstruction attacks on machine unlearning: Simple models are vulnerable
Martin Bertran Lopez, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu

Retrieval-augmented generation (RAG)

RAGChecker: A fine-grained framework for diagnosing retrieval-augmented generation
Dongyu Ru, Lin Qiu, Xiangkun Hu, Tianhang Zhang, Peng Shi, Shuaichen Chang, Cheng Jiayang, Cunxiang Wang, Shichao Sun, Huanyu Li, Zizhao Zhang, Binjie Wang, Jiarong Jiang, Tong He, Zhiguo Wang, Pengfei Liu, Yue Zhang, Zheng Zhang

Speech processing

CA-SSLR: Condition-aware self-supervised learning representation for generalized speech processing
Yen-Ju Lu, Jing Liu, Thomas Thebaud, Laureano Moro-Velazquez, Ariya Rastrow, Najim Dehak, Jesus Villalba

CA-SSLR scheme
The CA-SSLR scheme and its time-channel attention conditioner, as proposed in "CA-SSLR: Condition-aware self-supervised learning representation for generalized speech processing". Only the conditioner and linear projections for the decoders are trainable; all other parameters are frozen during adaptation. CA-SSLR improves SSL features by integrating intermediate LID/SV conditions, keeping pretrained parameters frozen (left). The trainable time-channel attention conditioner integrates language and speaker prediction (right).

Training methods

CoMERA: Computing- and memory-efficient training via rank-adaptive tensor optimization
Zi Yang, Ziyue Liu, Samridhi Choudhary, Xinfeng Xie, Cao Gao, Siegfried Kunzmann, Zheng Zhang

Optimal design for human preference elicitation
Subhojyoti Mukherjee, Anusha Lalitha, Kousha Kalantari, Aniket Deshmukh, Ge Liu, Yifei Ma, Branislav Kveton

Rejection via learning density ratios
Alexander Soen, Hisham Husain, Philip Schulz, Vu Nguyen

Unraveling the gradient descent dynamics of transformers
Bingqing Song, Boran Han, Shuai Zhang, Jie Ding, Mingyi Hong

Video

One token to seg them all: Language instructed reasoning segmentation in videos
Zechen Bai, Tong He, Haiyang Mei, Pichao Wang, Ziteng Gao, Joya Chen, Lei Liu, Pichao Wang, Zheng Zhang, Mike Zheng Shou

One token to seg them all: Language instructed reasoning segmentation in videos
The video object segmentation framework proposed in "One token to seg them all: Language instructed reasoning segmentation in videos".

Video token merging for long-form video understanding
Seon Ho Lee, Jue Wang, Zhikang Zhang, David Fan, Xinyu (Arthur) Li

Vision-language models

Unified lexical representation for interpretable visual-language alignment
Yifan Li, Yikai Wang, Yanwei Fu, Dongyu Ru, Zheng Zhang, Tong He

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The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
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