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A quick guide to Amazon’s papers at ACL 2024

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

By Staff writer
August 12, 2024
5 min read
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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".

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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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