21 citations · 29 across the 12 of their papers we have counts for
14 papers
SpeedrunBench: Challenging LLM Agents with Video Game Speedrunning
Yoshinari Fujinuma, Keisuke Kamahori, Ryuto Koike +8
Frontier LLM agents have been shown to be capable of solving increasingly complex tasks for which humans have measurable solutions. This begs the pertinent question of whether LLM…
FigmaTrace: Capturing Creative Nuances in Human Figma Design Workflows
Darshan Deshpande, Yoshinari Fujinuma, Martyna Markiewicz +5
Vision Language Models have recently shown improvements in several objective and verifiable domains such as object detection but continue to underperform on subjective and creative…
Defenses & Enablers For Skill Injection Attacks on Terminal Based Agents
Yoshinari Fujinuma, Varun Gangal, Traian Rebedea +4
Large language model (LLM) agents increasingly rely on reusable skills i.e. documents describing task-specific procedures. However, this introduces a new attack surface for agents…
Unlocking Prompt Infilling Capability for Diffusion Language Models
Yoshinari Fujinuma, Keisuke Sakaguchi
Masked diffusion language models (dLMs) generate text through bidirectional denoising, yet this capability remains locked for infilling prompts. This limitation is an artifact of t…
Contrastive Decoding Mitigates Score Range Bias in LLM-as-a-Judge
Yoshinari Fujinuma
Large Language Models (LLMs) are commonly used as evaluators in various applications, but the reliability of the outcomes remains a challenge. One such challenge is using LLMs-as-j…
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation
Benjamin Hsu, Xiaoyu Liu, Huayang Li +6
Document translation poses a challenge for Neural Machine Translation (NMT) systems. Most document-level NMT systems rely on meticulously curated sentence-level parallel data, assu…
A Multi-Modal Multilingual Benchmark for Document Image Classification
Yoshinari Fujinuma, Siddharth Varia, Nishant Sankaran +3
Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents su…
Comparing Biases and the Impact of Multilingual Training across Multiple Languages
Sharon Levy, Neha Anna John, Ling Liu +6
Studies in bias and fairness in natural language processing have primarily examined social biases within a single language and/or across few attributes (e.g. gender, race). However…
Diable: Efficient Dialogue State Tracking as Operations on Tables
Pietro Lesci, Yoshinari Fujinuma, Momchil Hardalov +3
Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, an…
Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability
Yoshinari Fujinuma, Jordan Boyd-Graber, Katharina Kann
Pretrained multilingual models enable zero-shot learning even for unseen languages, and that performance can be further improved via adaptation prior to finetuning. However, it is…
Semi-Supervised Joint Estimation of Word and Document Readability
Yoshinari Fujinuma, Masato Hagiwara
Readability or difficulty estimation of words and documents has been investigated independently in the literature, often assuming the existence of extensive annotated resources for…
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries
Mozhi Zhang, Yoshinari Fujinuma, Michael J. Paul +1
Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI). Recent CLWE methods use linear projections, which underfit the training dictionary, t…
A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity
Yoshinari Fujinuma, Jordan Boyd-Graber, Michael J. Paul
Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. An important requirement for many downstream tasks is that w…
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification
Mozhi Zhang, Yoshinari Fujinuma, Jordan Boyd-Graber
Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigat…