66 citations · 435 across the 34 of their papers we have counts for
38 papers
LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation
Keisuke Kamahori, Jungo Kasai, Noriyuki Kojima +1
Modern automatic speech recognition (ASR) models, such as OpenAI's Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient d…
Summarization-Based Document IDs for Generative Retrieval with Language Models
Haoxin Li, Daniel Cheng, Phillip Keung +2
Generative retrieval (Wang et al., 2022; Tay et al., 2022) is a popular approach for end-to-end document retrieval that directly generates document identifiers given an input query…
Evaluating Spatial Understanding of Large Language Models
Yutaro Yamada, Yihan Bao, Andrew K. Lampinen +2
Large language models (LLMs) show remarkable capabilities across a variety of tasks. Despite the models only seeing text in training, several recent studies suggest that LLM repres…
Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence
John J. Nay, David Karamardian, Sarah B. Lawsky +6
Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and…
Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models
Orevaoghene Ahia, Sachin Kumar, Hila Gonen +4
Language models have graduated from being research prototypes to commercialized products offered as web APIs, and recent works have highlighted the multilingual capabilities of the…
Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations
Jungo Kasai, Yuhei Kasai, Keisuke Sakaguchi +2
As large language models (LLMs) gain popularity among speakers of diverse languages, we believe that it is crucial to benchmark them to better understand model behaviors, failures,…
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
Yushi Hu, Benlin Liu, Jungo Kasai +4
Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align w…
Batch Prompting: Efficient Inference with Large Language Model APIs
Zhoujun Cheng, Jungo Kasai, Tao Yu
Performing inference on large volumes of samples with large language models (LLMs) can be computationally and financially costly in industry and real-world use. We propose batch pr…
NarrowBERT: Accelerating Masked Language Model Pretraining and Inference
Haoxin Li, Phillip Keung, Daniel Cheng +2
Large-scale language model pretraining is a very successful form of self-supervised learning in natural language processing, but it is increasingly expensive to perform as the mode…
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Hongjin Su, Weijia Shi, Jungo Kasai +7
We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g.,…
BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting
Zheng-Xin Yong, Hailey Schoelkopf, Niklas Muennighoff +12
The BLOOM model is a large publicly available multilingual language model, but its pretraining was limited to 46 languages. To extend the benefits of BLOOM to other languages witho…
How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers
Michael Hassid, Hao Peng, Daniel Rotem +4
The attention mechanism is considered the backbone of the widely-used Transformer architecture. It contextualizes the input by computing input-specific attention matrices. We find…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Selective Annotation Makes Language Models Better Few-Shot Learners
Hongjin Su, Jungo Kasai, Chen Henry Wu +8
Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they l…
FOLIO: Natural Language Reasoning with First-Order Logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao +32
Large language models (LLMs) have achieved remarkable performance on a variety of natural language understanding tasks. However, existing benchmarks are inadequate in measuring the…
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
Akari Asai, Shayne Longpre, Jungo Kasai +6
We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typo…
RealTime QA: What's the Answer Right Now?
Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi +7
We introduce REALTIME QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). REALTIME QA inquire…
Twist Decoding: Diverse Generators Guide Each Other
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras +5
Many language generation models are now available for a wide range of generation tasks, including machine translation and summarization. Combining such diverse models may lead to f…
A Call for Clarity in Beam Search: How It Works and When It Stops
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras +3
Text generation with beam search has proven successful in a wide range of applications. We point out that, though largely overlooked in the literature, the commonly-used implementa…
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
Ximing Lu, Sean Welleck, Peter West +9
The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constra…
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras +5
Natural language processing researchers have identified limitations of evaluation methodology for generation tasks, with new questions raised about the validity of automatic metric…
Transparent Human Evaluation for Image Captioning
Jungo Kasai, Keisuke Sakaguchi, Lavinia Dunagan +4
We establish THumB, a rubric-based human evaluation protocol for image captioning models. Our scoring rubrics and their definitions are carefully developed based on machine- and hu…
ABC: Attention with Bounded-memory Control
Hao Peng, Jungo Kasai, Nikolaos Pappas +5
Transformer architectures have achieved state-of-the-art results on a variety of sequence modeling tasks. However, their attention mechanism comes with a quadratic complexity in se…
One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval
Akari Asai, Xinyan Yu, Jungo Kasai +1
We present Cross-lingual Open-Retrieval Answer Generation (CORA), the first unified many-to-many question answering (QA) model that can answer questions across many languages, even…