519 citations · 852 across the 15 of their papers we have counts for
19 papers
Training Reasoning Models to Generate Diverse Scientific Ideas
Manya Wadhwa, Pranav Narayanan Venkit, Hiroaki Hayashi +3
One of the first steps of a research pipeline is generating ideas. Large Language Models (LLMs) are increasingly used to automate this step, but most of this work optimizes the qua…
Agentic Self-Awareness: Aligning Knowing, Acting, and Holding
Qinglin Chen, Hiroaki Hayashi, Jiaxin Zhang +1
Frontier AI agents increasingly act in environments where seemingly solvable tasks may lack prerequisites, contain conflicting requirements, or reveal blockers only during executio…
Self-Retrospection Distillation: Turning Post-hoc Experiences into Prior Foresight
Haoxiang Zhang, Qinglin Chen, Hiroaki Hayashi +9
Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after interaction. For group-relative ob…
Prospective Hindsight: Self-Calibrating Reinforcement Learning via Prediction-Reality Gaps
Jiaxin Zhang, Xiangyu Peng, Qinglin Chen +3
Reinforcement learning for long-horizon agents relies on purely retrospective training signals: credit is assigned only after observing environmental consequences, leaving the agen…
Redesigning and Auditing Deep Research Writing for Faithful Reports
Hiroaki Hayashi, Pranav Narayanan Venkit, Prafulla Kumar Choubey +1
Rubric-based evaluations of deep-research (DR) systems often obscure fine-grained factual failures in generated reports. We introduce CLAIMPROBE, a claim-level audit that decompose…
XGen-7B Technical Report
Erik Nijkamp, Tian Xie, Hiroaki Hayashi +22
Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LL…
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong +2
Large language models (LLMs) have demonstrated remarkable abilities in representation learning for program synthesis and understanding tasks. The quality of the learned representat…
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi +5
Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The…
DEEP: DEnoising Entity Pre-training for Neural Machine Translation
Junjie Hu, Hiroaki Hayashi, Kyunghyun Cho +1
It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation…
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu +3
This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, whic…
WikiAsp: A Dataset for Multi-domain Aspect-based Summarization
Hiroaki Hayashi, Prashant Budania, Peng Wang +3
Aspect-based summarization is the task of generating focused summaries based on specific points of interest. Such summaries aid efficient analysis of text, such as quickly understa…
What's New? Summarizing Contributions in Scientific Literature
Hiroaki Hayashi, Wojciech Kryściński, Bryan McCann +2
With thousands of academic articles shared on a daily basis, it has become increasingly difficult to keep up with the latest scientific findings. To overcome this problem, we intro…
GSum: A General Framework for Guided Neural Abstractive Summarization
Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi +2
Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies a…
Findings of the Third Workshop on Neural Generation and Translation
Hiroaki Hayashi, Yusuke Oda, Alexandra Birch +5
This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Lang…
Linguistic Versus Latent Relations for Modeling Coherent Flow in Paragraphs
Dongyeop Kang, Hiroaki Hayashi, Alan W Black +1
Generating a long, coherent text such as a paragraph requires a high-level control of different levels of relations between sentences (e.g., tense, coreference). We call such a log…
Latent Relation Language Models
Hiroaki Hayashi, Zecong Hu, Chenyan Xiong +1
In this paper, we propose Latent Relation Language Models (LRLMs), a class of language models that parameterizes the joint distribution over the words in a document and the entitie…
Learning to Describe Phrases with Local and Global Contexts
Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga +4
When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entit…
Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates
Hiroaki Hayashi, Jayanth Koushik, Graham Neubig
Adaptive gradient methods for stochastic optimization adjust the learning rate for each parameter locally. However, there is also a global learning rate which must be tuned in orde…