A quick guide to Amazon’s papers at ACL 2024

Work on large language models predominates, with a particular focus on model evaluation.

Like the field of conversational AI in general, Amazon’s papers at this year’s meeting of the Association for Computational Linguistics (ACL) are dominated by work on large language models (LLMs). The properties that make LLMs’ outputs so extraordinary — such as their linguistic fluency and semantic coherence — are also notoriously difficult to quantify; as such, model evaluation has emerged as a particular area of focus. But Amazon’s papers explore a wide range of LLM-related topics, from applications such as code synthesis and automatic speech recognition to problems of LLM training and deployment, such as continual pretraining and hallucination mitigation. Papers accepted to the recently inaugurated Proceedings of the ACL are marked with asterisks.

Code synthesis

Fine-tuning language models for joint rewriting and completion of code with potential bugs
Dingmin Wang, Jinman Zhao, Hengzhi Pei, Samson Tan, Sheng Zha

Bug injection.png
Obtaining buggy partial code via bug injection. From “Fine-tuning language models for joint rewriting and completion of code with potential bugs”.

Continual pretraining

Efficient continual pre-training for building domain specific large language models*
Yong Xie, Karan Aggarwal, Aitzaz Ahmad

Data quality

A shocking amount of the web is machine translated: Insights from multi-way parallelism*
Brian Thompson, Mehak Dhaliwal, Peter Frisch, Tobias Domhan, Marcello Federico

Document summarization

The power of summary-source alignments
Ori Ernst, Ori Shapira, Aviv Slobodkin, Sharon Adar, Mohit Bansal, Jacob Goldberger, Ran Levy, Ido Dagan

Hallucination mitigation

Learning to generate answers with citations via factual consistency models
Rami Aly, Zhiqiang Tang, Samson Tan, George Karypis

Intent classification

Can your model tell a negation from an implicature? Unravelling challenges with intent encoders
Yuwei Zhang, Siffi Singh, Sailik Sengupta, Igor Shalyminov, Hwanjun Song, Hang Su, Saab Mansour

Irony recognition

MultiPICo: Multilingual perspectivist irony corpus
Silvia Casola, Simona Frenda, Soda Marem Lo, Erhan Sezerer, Antonio Uva, Valerio Basile, Cristina Bosco, Alessandro Pedrani, Chiara Rubagotti, Viviana Patti, Davide Bernardi

Knowledge grounding

Graph chain-of-thought: Augmenting large language models by reasoning on graphs
Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, Jiawei Han

MATTER: Memory-augmented transformer using heterogeneous knowledge sources*
Dongkyu Lee, Chandana Satya Prakash, Jack G. M. FitzGerald, Jens Lehmann

Tree-of-traversals: A zero-shot reasoning algorithm for augmenting black-box language models with knowledge graphs
Elan Markowitz, Anil Ramakrishna, Jwala Dhamala, Ninareh Mehrabi, Charith Peris, Rahul Gupta, Kai-Wei Chang, Aram Galstyan

Tree of traversals.png
An example of how the tree-of-traversals method uses a knowledge graph interface for the query “What actor played in both Inception and Interstellar?” From "Tree-of-traversals: A zero-shot reasoning algorithm for augmenting black-box language models with knowledge graphs".

LLM decoding

BASS: Batched attention-optimized speculative sampling*
Haifeng Qian, Sujan Gonugondla, Sungsoo Ha, Mingyue Shang, Sanjay Krishna Gouda, Ramesh Nallapati, Sudipta Sengupta, Anoop Deoras

Machine translation

Impacts of misspelled queries on translation and product search
Greg Hanneman, Natawut Monaikul, Taichi Nakatani

The fine-tuning paradox: Boosting translation quality without sacrificing LLM abilities
David Stap, Eva Hasler, Bill Byrne, Christof Monz, Ke Tran

Model editing

Propagation and pitfalls: Reasoning-based assessment of knowledge editing through counterfactual tasks
Wenyue Hua, Jiang Guo, Marvin Dong, Henghui Zhu, Patrick Ng, Zhiguo Wang

ReCoE construction.png
Demonstration of the process used to construct data for the reasoning-based counterfactual-editing (ReCoE) dataset. Straight lines represent data sourced from existing datasets; dashed lines denote data derived from LLM generation; zigzag lines denote data obtained through the corruption of other data. From "Propagation and pitfalls: Reasoning-based assessment of knowledge editing through counterfactual tasks".

Model evaluation

Bayesian prompt ensembles: Model uncertainty estimation for black-box large language models
Francesco Tonolini, Jordan Massiah, Nikolaos Aletras, Gabriella Kazai

ConSiDERS—the-human evaluation framework: Rethinking human evaluation for generative large language models
Aparna Elangovan, Ling Liu, Lei Xu, Sravan Bodapati, Dan Roth

Factual confidence of LLMs: On reliability and robustness of current estimators
Matéo Mahaut, Laura Aina, Paula Czarnowska, Momchil Hardalov, Thomas Müller, Lluís Marquez

Fine-tuned machine translation metrics struggle in unseen domains
Vilém Zouhar, Shuoyang Ding, Anna Currey, Tatyana Badeka, Jenyuan Wang, Brian Thompson

Measuring question answering difficulty for retrieval-augmented generation
Matteo Gabburo, Nicolaas Jedema, Siddhant Garg, Leonardo Ribeiro, Alessandro Moschitti

Model robustness

Extreme miscalibration and the illusion of adversarial robustness
Vyas Raina, Samson Tan, Volkan Cevher, Aditya Rawal, Sheng Zha, George Karypis

Multimodal models

CaMML: Context-aware multimodal learner for large models
Yixin Chen, Shuai Zhang, Boran Han, Tong He, Bo Li

CAMML.png
The CaMML framework, which consists of a retriever, a perceiver and a generator. After receiving user query q, the CaMML retriever identifies relevant multimodal contexts C from the data store. Then the CaMML perceiver seamlessly integrates data of various modalities, effectively encoding long-context information and injecting it into the CaMML generator. This enables the prediction of responses that are conditioned on both the context and the query. From "CaMML: Context-aware multimodal learner for large models".

Multi-modal retrieval for large language model based speech recognition
Jari Kolehmainen, Aditya Gourav, Prashanth Gurunath Shivakumar, Yi Gu, Ankur Gandhe, Ariya Rastrow, Grant Strimel, Ivan Bulyko

REFINESUMM: Self-refining MLLM for generating a multimodal summarization dataset
Vaidehi Patil, Leonardo Ribeiro, Mengwen Liu, Mohit Bansal, Markus Dreyer

Ordinal classification

Exploring ordinality in text classification: A comparative study of explicit and implicit techniques
Siva Rajesh Kasa, Aniket Goel, Sumegh Roychowdhury, Karan Gupta, Anish Bhanushali, Nikhil Pattisapu, Prasanna Srinivasa Murthy

Question answering

Beyond boundaries: A human-like approach for question answering over structured and unstructured information sources*
Jens Lehmann, Dhananjay Bhandiwad, Preetam Gattogi, Sahar Vahdati

MinPrompt: Graph-based minimal prompt data augmentation for few-shot question answering
Xiusi Chen, Jyun-Yu Jiang, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Wei Wang

Synthesizing conversations from unlabeled documents using automatic response segmentation
Fanyou Wu, Weijie Xu, Chandan Reddy, Srinivasan Sengamedu, "SHS"

Reasoning

Eliciting better multilingual structured reasoning from LLMs through code
Bryan Li, Tamer Alkhouli, Daniele Bonadiman, Nikolaos Pappas, Saab Mansour

II-MMR: Identifying and improving multi-modal multi-hop reasoning in visual question answering*
Jihyung Kil, Farideh Tavazoee, Dongyeop Kang, Joo-Kyung Kim

Recommender systems

Generative explore-exploit: Training-free optimization of generative recommender systems using LLM optimizers
Besnik Fetahu, Zhiyu Chen, Davis Yoshida, Giuseppe Castellucci, Nikhita Vedula, Jason Choi, Shervin Malmasi

Towards translating objective product attributes into customer language
Ram Yazdi, Oren Kalinsky, Alexander Libov, Dafna Shahaf

Responsible AI

SpeechGuard: Exploring the adversarial robustness of multimodal large language models
Raghuveer Peri, Sai Muralidhar Jayanthi, Srikanth Ronanki, Anshu Bhatia, Karel Mundnich, Saket Dingliwal, Nilaksh Das, Zejiang Hou, Goeric Huybrechts, Srikanth Vishnubhotla, Daniel Garcia-Romero, Sundararajan Srinivasan, Kyu Han, Katrin Kirchhoff

Text completion

Token alignment via character matching for subword completion*
Ben Athiwaratkun, Shiqi Wang, Mingyue Shang, Yuchen Tian, Zijian Wang, Sujan Gonugondla, Sanjay Krishna Gouda, Rob Kwiatkowski, Ramesh Nallapati, Bing Xiang

Token alignment.png
An illustration of token alignment process presented in "Token alignment via character matching for subword completion".

Research areas

Related content

US, NY, New York
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist to work on pre-training methodologies for Generative Artificial Intelligence (GenAI) models. You will interact closely with our customers and with the academic and research communities. Key job responsibilities Join us to work as an integral part of a team that has experience with GenAI models in this space. We work on these areas: - Scaling laws - Hardware-informed efficient model architecture, low-precision training - Optimization methods, learning objectives, curriculum design - Deep learning theories on efficient hyperparameter search and self-supervised learning - Learning objectives and reinforcement learning methods - Distributed training methods and solutions - AI-assisted research About the team The AGI team has a mission to push the envelope in GenAI with Large Language Models (LLMs) and multimodal systems, in order to provide the best-possible experience for our customers.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities - Develop ML models for various recommendation & search systems using deep learning, online learning, and optimization methods - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences.
US, NY, New York
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Publish your research findings in top conferences and journals. About the team Prime Video Search Science team owns science solution to power search experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering teams to launch our solutions in production.
US, CA, San Francisco
If you are interested in this position, please apply on Twitch's Career site https://www.twitch.tv/jobs/en/ About Us: Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X, and discover the projects we’re solving on our Blog. Be sure to explore our Interviewing Guide to learn how to ace our interview process. You can work in San Francisco, CA or Seattle, WA. Perks - Medical, Dental, Vision & Disability Insurance - 401(k) - Maternity & Parental Leave - Flexible PTO - Amazon Employee Discount
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with world-class scientists and engineers to develop novel data, modeling and engineering solutions to support the responsible AI initiatives at AGI. Your work will directly impact our customers in the form of products and services that make use of audio technology. About the team While the rapid advancements in Generative AI have captivated global attention, we see these as just the starting point. Our team is dedicated to pushing the boundaries of what’s possible, leveraging Amazon’s unparalleled ML infrastructure, computing resources, and commitment to responsible AI principles. And Amazon’s leadership principle of customer obsession guides our approach, prioritizing our customers’ needs and preferences each step of the way.
US, WA, Bellevue
Are you interested in a unique opportunity to advance the accuracy and efficiency of Artificial General Intelligence (AGI) systems? If so, you're at the right place! As a Quantitative Researcher on our team, you will be working at the intersection of mathematics, computer science, and finance, you will collaborate with a diverse team of engineers in a fast-paced, intellectually challenging environment where innovative thinking is encouraged and rewarded. We operate at Amazon's large scale with the energy of a nimble start-up. If you have a learner's mindset, enjoy solving challenging problems, and value an inclusive team culture, you will thrive in this role, and we hope to hear from you. Key job responsibilities * Conduct statistical analyses on web-scale datasets to develop state-of-the-art multimodal large language models * Conceptualize and develop mathematical models, data sampling and preparation strategies to continuously improve existing algorithms * Identify and utilize data sources to drive innovation and improvements to our LLMs About the team We are passionate engineers and scientists dedicated to pushing the boundaries of innovation. We evaluate and represent the customer perspective through accurate benchmarking.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Senior Applied Scientist, to lead the development and implementation of algorithms and models for supervised fine-tuning and reinforcement learning through human feedback; with a focus across text, image, and video modalities. As a Senior Applied Scientist, you will play a critical role in driving the development of Generative AI (Gen AI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of GenAI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team
MX, DIF, Mexico City
Do you like working on projects that are highly visible and are tied closely to Amazon’s growth? Are you seeking an environment where you can drive innovation leveraging the scalability and innovation with Amazon's AWS cloud services? The Amazon International Technology Team is hiring Applied Scientists to work in our Machine Learning team in Mexico City. The Intech team builds International extensions and new features of the Amazon.com web site for individual countries and creates systems to support Amazon operations. We have already worked in Germany, France, UK, India, China, Italy, Brazil and more. Key job responsibilities About you You want to make changes that help millions of customers. You don’t want to make something 10% better as a part of an enormous team. Rather, you want to innovate with a small community of passionate peers. You have experience in analytics, machine learning, LLMs and Agentic AI, and a desire to learn more about these subjects. You want a trusted role in strategy and product design. You put the customer first in your thinking. You have great problem solving skills. You research the latest data technologies and use them to help you innovate and keep costs low. You have great judgment and communication skills, and a history of delivering results. Your Responsibilities - Define and own complex machine learning solutions in the consumer space, including targeting, measurement, creative optimization, and multivariate testing. - Design, implement, and evolve Agentic AI systems that can autonomously perceive their environment, reason about context, and take actions across business workflows—while ensuring human-in-the-loop oversight for high-stakes decisions. - Influence the broader team's approach to integrating machine learning into business workflows. - Advise leadership, both tech and non-tech. - Support technical trade-offs between short-term needs and long-term goals.
BR, SP, Sao Paulo
Do you like working on projects that are highly visible and are tied closely to Amazon’s growth? Are you seeking an environment where you can drive innovation leveraging the scalability and innovation with Amazon's AWS cloud services? The Amazon International Technology Team is hiring Applied Scientists to work in our Machine Learning team in Mexico City. The Intech team builds International extensions and new features of the Amazon.com web site for individual countries and creates systems to support Amazon operations. We have already worked in Germany, France, UK, India, China, Italy, Brazil and more. Key job responsibilities About you You want to make changes that help millions of customers. You don’t want to make something 10% better as a part of an enormous team. Rather, you want to innovate with a small community of passionate peers. You have experience in analytics, machine learning, LLMs and Agentic AI, and a desire to learn more about these subjects. You want a trusted role in strategy and product design. You put the customer first in your thinking. You have great problem solving skills. You research the latest data technologies and use them to help you innovate and keep costs low. You have great judgment and communication skills, and a history of delivering results. Your Responsibilities - Define and own complex machine learning solutions in the consumer space, including targeting, measurement, creative optimization, and multivariate testing. - Design, implement, and evolve Agentic AI systems that can autonomously perceive their environment, reason about context, and take actions across business workflows—while ensuring human-in-the-loop oversight for high-stakes decisions. - Influence the broader team's approach to integrating machine learning into business workflows. - Advise leadership, both tech and non-tech. - Support technical trade-offs between short-term needs and long-term goals.
BR, SP, Sao Paulo
Do you like working on projects that are highly visible and are tied closely to Amazon’s growth? Are you seeking an environment where you can drive innovation leveraging the scalability and innovation with Amazon's AWS cloud services? The Amazon International Technology Team is hiring Applied Scientists to work in our Software Development Center in Sao Paulo. The Intech team builds International extensions and new features of the Amazon.com web site for individual countries and creates systems to support Amazon operations. We have already worked in Germany, France, UK, India, China, Italy, Brazil and more. Key job responsibilities About you You want to make changes that help millions of customers. You don’t want to make something 10% better as a part of an enormous team. Rather, you want to innovate with a small community of passionate peers. You have experience in analytics, machine learning and big data, and a desire to learn more about these subjects. You want a trusted role in strategy and product design. You put the customer first in your thinking. You have great problem solving skills. You research the latest data technologies and use them to help you innovate and keep costs low. You have great judgment and communication skills, and a history of delivering results. Your Responsibilities - Define and own complex machine learning solutions in the consumer space, including targeting, measurement, creative optimization, and multivariate testing. - Influence the broader team's approach to integrating machine learning into business workflows. - Advise senior leadership, both tech and non-tech. - Make technical trade-offs between short-term needs and long-term goals.