9 citations · 31 across the 72 of their papers we have counts for
63 papers · 1 filter
Quit While You're Ahead: Quit for Efficient Candidate Generation in Machine Translation Reranking
Guangyu Chen, Boxuan Lyu, Hidetaka Kamigaito +2
Reranking methods, such as Minimum Bayes Risk (MBR) decoding and Quality Estimation (QE) reranking, have been widely used in modern neural machine translation (NMT) to select an ou…
Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation
Boxuan Lyu, Haiyue Song, Zhi Qu +3
Prior work has explored prompting large language models (LLMs) to rewrite source text before translation, with the goal of improving machine translation (MT) quality. However, we f…
Oogiri-Master: Benchmarking Humor Understanding via Oogiri
Soichiro Murakami, Hidetaka Kamigaito, Hiroya Takamura +1
Humor is a salient testbed for human-like creative thinking in large language models (LLMs). We study humor using the Japanese creative response game Oogiri, in which participants…
VLURes: Benchmarking VLM Visual and Linguistic Understanding in Low-Resource Languages
Jesse Atuhurra, Iqra Ali, Tomoya Iwakura +2
Vision Language Models (VLMs) are pivotal for advancing perception in intelligent agents. Yet, evaluation of VLMs remains limited to predominantly English-centric benchmarks in whi…
Decoding Uncertainty: The Impact of Decoding Strategies for Uncertainty Estimation in Large Language Models
Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe
Decoding strategies manipulate the probability distribution underlying the output of a language model and can therefore affect both generation quality and its uncertainty. In this…
SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala
Ashmari Pramodya, Nirasha Nelki, Heshan Shalinda +6
Large Language Models (LLMs) demonstrate impressive general knowledge and reasoning abilities, yet their evaluation has predominantly focused on global or anglocentric subjects, of…
From Formal Language Theory to Statistical Learning: Finite Observability of Subregular Languages
Katsuhiko Hayashi, Hidetaka Kamigaito
We prove that all standard subregular language classes are linearly separable when represented by their deciding predicates. This establishes finite observability and guarantees le…
Length Representations in Large Language Models
Sangjun Moon, Dasom Choi, Jingun Kwon +2
Large language models (LLMs) have shown remarkable capabilities across various tasks, that are learned from massive amounts of text-based data. Although LLMs can control output seq…
CodeNER: Code Prompting for Named Entity Recognition
Sungwoo Han, Hyeyeon Kim, Jingun Kwon +2
Recent studies have explored various approaches for treating candidate named entity spans as both source and target sequences in named entity recognition (NER) by leveraging large…
Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
In generative commonsense reasoning tasks such as CommonGen, generative large language models (LLMs) compose sentences that include all given concepts. However, when focusing on in…
Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws
Hidetaka Kamigaito, Ying Zhang, Jingun Kwon +3
Transformers deliver outstanding performance across a wide range of tasks and are now a dominant backbone architecture for large language models (LLMs). Their task-solving performa…
Dictionaries to the Rescue: Cross-Lingual Vocabulary Transfer for Low-Resource Languages Using Bilingual Dictionaries
Haruki Sakajo, Yusuke Ide, Justin Vasselli +4
Cross-lingual vocabulary transfer plays a promising role in adapting pre-trained language models to new languages, including low-resource languages. Existing approaches that utiliz…
AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset
Soichiro Murakami, Peinan Zhang, Hidetaka Kamigaito +2
Identifying factors that make ad text attractive is essential for advertising success. This study proposes AdParaphrase v2.0, a dataset for ad text paraphrasing, containing human p…
IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generation
Kazuki Hayashi, Hidetaka Kamigaito, Shinya Kouda +1
Retrieval-Augmented Generation (RAG) has emerged as a way to complement the in-context knowledge of Large Language Models (LLMs) by integrating external documents. However, real-wo…
TextTIGER: Text-based Intelligent Generation with Entity Prompt Refinement for Text-to-Image Generation
Shintaro Ozaki, Tomoyuki Jinno, Kazuki Hayashi +6
When generating images from prompts that include specific entities, the model must retain as much entity-specific knowledge as possible. However, the number of entities is almost c…
Long-Tail Crisis in Nearest Neighbor Language Models
Yuto Nishida, Makoto Morishita, Hiroyuki Deguchi +2
The -nearest-neighbor language model (NN-LM), one of the retrieval-augmented language models, improves the perplexity for given text by directly accessing a large datastore b…
Considering Length Diversity in Retrieval-Augmented Summarization
Juseon-Do, Jaesung Hwang, Jingun Kwon +2
This study investigates retrieval-augmented summarization by specifically examining the impact of exemplar summary lengths under length constraints, not covered by previous work. W…
AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts
Soichiro Murakami, Peinan Zhang, Hidetaka Kamigaito +2
Effective linguistic choices that attract potential customers play crucial roles in advertising success. This study aims to explore the linguistic features of ad texts that influen…
Tonguescape: Exploring Language Models Understanding of Vowel Articulation
Haruki Sakajo, Yusuke Sakai, Hidetaka Kamigaito +1
Vowels are primarily characterized by tongue position. Humans have discovered these features of vowel articulation through their own experience and explicit objective observation s…
Can Impressions of Music be Extracted from Thumbnail Images?
Takashi Harada, Takehiro Motomitsu, Katsuhiko Hayashi +2
In recent years, there has been a notable increase in research on machine learning models for music retrieval and generation systems that are capable of taking natural language sen…
Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain
Shintaro Ozaki, Yuta Kato, Siyuan Feng +8
Retrieval Augmented Generation (RAG) complements the knowledge of Large Language Models (LLMs) by leveraging external information to enhance response accuracy for queries. This app…
CoAM: Corpus of All-Type Multiword Expressions
Yusuke Ide, Joshua Tanner, Adam Nohejl +4
Multiword expressions (MWEs) refer to idiomatic sequences of multiple words. MWE identification, i.e., detecting MWEs in text, can play a key role in downstream tasks such as machi…
NERsocial: Efficient Named Entity Recognition Dataset Construction for Human-Robot Interaction Utilizing RapidNER
Jesse Atuhurra, Hidetaka Kamigaito, Hiroki Ouchi +2
Adapting named entity recognition (NER) methods to new domains poses significant challenges. We introduce RapidNER, a framework designed for the rapid deployment of NER systems thr…
Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decoding
Hidetaka Kamigaito, Hiroyuki Deguchi, Yusuke Sakai +2
Inference methods play an important role in eliciting the performance of large language models (LLMs). Currently, LLMs use inference methods utilizing generated multiple samples, w…