607 citations · 1.4k across the 40 of their papers we have counts for
31 papers · 1 filter
Multimodal RewardBench 2: Evaluating Omni Reward Models for Interleaved Text and Image
Yushi Hu, Reyhane Askari-Hemmat, Melissa Hall +3
Reward models (RMs) are essential for training large language models (LLMs), but remain underexplored for omni models that handle interleaved image and text sequences. We introduce…
reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputs
Zhaofeng Wu, Michihiro Yasunaga, Andrew Cohen +3
Reward models have become a staple in modern NLP, serving as not only a scalable text evaluator, but also an indispensable component in many alignment recipes and inference-time al…
ALMA: Alignment with Minimal Annotation
Michihiro Yasunaga, Leonid Shamis, Chunting Zhou +4
Recent approaches to large language model (LLM) alignment typically require millions of human annotations or rely on external aligned models for synthetic data generation. This pap…
JPEG-LM: LLMs as Image Generators with Canonical Codec Representations
Xiaochuang Han, Marjan Ghazvininejad, Pang Wei Koh +1
Recent work in image and video generation has been adopting the autoregressive LLM architecture due to its generality and potentially easy integration into multi-modal systems. The…
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
David helps Goliath: Inference-Time Collaboration Between Small Specialized and Large General Diffusion LMs
Xiaochuang Han, Sachin Kumar, Yulia Tsvetkov +1
Diffusion-based language models are emerging as a promising alternative to autoregressive LMs: they approach the competence of autoregressive LMs while offering nuanced controllabi…
Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation
Marjan Ghazvininejad, Hila Gonen, Luke Zettlemoyer
Large language models (LLMs) demonstrate remarkable machine translation (MT) abilities via prompting, even though they were not explicitly trained for this task. However, even give…
Representation Deficiency in Masked Language Modeling
Yu Meng, Jitin Krishnan, Sinong Wang +6
Masked Language Modeling (MLM) has been one of the most prominent approaches for pretraining bidirectional text encoders due to its simplicity and effectiveness. One notable concer…
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models
Davis Liang, Hila Gonen, Yuning Mao +5
Large multilingual language models typically rely on a single vocabulary shared across 100+ languages. As these models have increased in parameter count and depth, vocabulary size…
In-context Examples Selection for Machine Translation
Sweta Agrawal, Chunting Zhou, Mike Lewis +2
Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning, where a few examples are used…
A Review on Language Models as Knowledge Bases
Badr AlKhamissi, Millicent Li, Asli Celikyilmaz +2
Recently, there has been a surge of interest in the NLP community on the use of pretrained Language Models (LMs) as Knowledge Bases (KBs). Researchers have shown that LMs trained o…
Natural Language to Code Translation with Execution
Freda Shi, Daniel Fried, Marjan Ghazvininejad +2
Generative models of code, pretrained on large corpora of programs, have shown great success in translating natural language to code (Chen et al., 2021; Austin et al., 2021; Li et…
Discourse-Aware Soft Prompting for Text Generation
Marjan Ghazvininejad, Vladimir Karpukhin, Vera Gor +1
Current efficient fine-tuning methods (e.g., adapters, prefix-tuning, etc.) have optimized conditional text generation via training a small set of extra parameters of the neural la…
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation
Eleftheria Briakou, Sida I. Wang, Luke Zettlemoyer +1
Mined bitexts can contain imperfect translations that yield unreliable training signals for Neural Machine Translation (NMT). While filtering such pairs out is known to improve fin…
Distributionally Robust Multilingual Machine Translation
Chunting Zhou, Daniel Levy, Xian Li +2
Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of…
Prompting Contrastive Explanations for Commonsense Reasoning Tasks
Bhargavi Paranjape, Julian Michael, Marjan Ghazvininejad +2
Many commonsense reasoning NLP tasks involve choosing between one or more possible answers to a question or prompt based on knowledge that is often implicit. Large pretrained langu…
EASE: Extractive-Abstractive Summarization with Explanations
Haoran Li, Arash Einolghozati, Srinivasan Iyer +4
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. To achieve…
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
Arun Babu, Akshat Shrivastava, Armen Aghajanyan +3
Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespr…
Detecting Hallucinated Content in Conditional Neural Sequence Generation
Chunting Zhou, Graham Neubig, Jiatao Gu +4
Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the inpu…
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation
Alexander R. Fabbri, Simeng Han, Haoyuan Li +5
Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. However, these models are typicall…
Pre-training via Paraphrasing
Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh +3
We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual multi-document paraphrasing objective. MARGE provides an alternative to the…
Aligned Cross Entropy for Non-Autoregressive Machine Translation
Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer +1
Non-autoregressive machine translation models significantly speed up decoding by allowing for parallel prediction of the entire target sequence. However, modeling word order is mor…
Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation
Asa Cooper Stickland, Xian Li, Marjan Ghazvininejad
There has been recent success in pre-training on monolingual data and fine-tuning on Machine Translation (MT), but it remains unclear how to best leverage a pre-trained model for a…
Semi-Autoregressive Training Improves Mask-Predict Decoding
Marjan Ghazvininejad, Omer Levy, Luke Zettlemoyer
The recently proposed mask-predict decoding algorithm has narrowed the performance gap between semi-autoregressive machine translation models and the traditional left-to-right appr…