28 citations · 55 across the 18 of their papers we have counts for
18 papers
Beyond Static Dialogues: Benchmarking Realistic, Heterogeneous, and Evolving Long-Term Memory
Han Zhang, Zihao Tang, Xin Yu +8
In existing memory benchmarks for Large Language Models (LLMs), the evaluated dialogue sessions often lack long-term semantic consistency, and the underlying personas tend to be fl…
Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing
Minglai Yang, Xinyan Velocity Yu, Pengyuan Li +22
Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems. However, existing Optical Character Recognition (OC…
EXACT: Explicit Attribute-Guided Decoding-Time Personalization
Xin Yu, Hanwen Xing, Lingzhou Xue
Achieving personalized alignment requires adapting large language models to each user's evolving context. While decoding-time personalization offers a scalable alternative to train…
Towards Better Optimization For Listwise Preference in Diffusion Models
Jiamu Bai, Xin Yu, Meilong Xu +6
Reinforcement learning from human feedback (RLHF) has proven effectiveness for aligning text-to-image (T2I) diffusion models with human preferences. Although Direct Preference Opti…
Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions
Wang Bill Zhu, Tianqi Chen, Xinyan Velocity Yu +6
Cancer patients are increasingly turning to large language models (LLMs) for medical information, making it critical to assess how well these models handle complex, personalized qu…
Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
Yuehan Qin, Shawn Li, Yi Nian +3
Large language models (LLMs) have shown substantial capacity for generating fluent, contextually appropriate responses. However, they can produce hallucinated outputs, especially w…
LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation
Fan Deng, Yaguang Wu, Xinyang Yu +4
Recently, text-to-image models based on diffusion have achieved remarkable success in generating high-quality images. However, the challenge of personalized, controllable generatio…
The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalities
Zhaofeng Wu, Xinyan Velocity Yu, Dani Yogatama +2
Modern language models can process inputs across diverse languages and modalities. We hypothesize that models acquire this capability through learning a shared representation space…
LocateBench: Evaluating the Locating Ability of Vision Language Models
Ting-Rui Chiang, Joshua Robinson, Xinyan Velocity Yu +1
The ability to locate an object in an image according to natural language instructions is crucial for many real-world applications. In this work we propose LocateBench, a high-qual…
CodeRAG-Bench: Can Retrieval Augment Code Generation?
Zora Zhiruo Wang, Akari Asai, Xinyan Velocity Yu +4
While language models (LMs) have proven remarkably adept at generating code, many programs are challenging for LMs to generate using their parametric knowledge alone. Providing ext…
Principled Gradient-based Markov Chain Monte Carlo for Text Generation
Li Du, Afra Amini, Lucas Torroba Hennigen +4
Recent papers have demonstrated the possibility of energy-based text generation by adapting gradient-based sampling algorithms, a paradigm of MCMC algorithms that promises fast con…
On Retrieval Augmentation and the Limitations of Language Model Training
Ting-Rui Chiang, Xinyan Velocity Yu, Joshua Robinson +3
Augmenting a language model (LM) with -nearest neighbors (NN) retrieval on its training data alone can decrease its perplexity, though the underlying reasons for this remain…
BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer
Akari Asai, Sneha Kudugunta, Xinyan Velocity Yu +6
Despite remarkable advancements in few-shot generalization in natural language processing, most models are developed and evaluated primarily in English. To facilitate research on f…
MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation
Swarnadeep Saha, Xinyan Velocity Yu, Mohit Bansal +2
Prompting large language models has enabled significant recent progress in multi-step reasoning over text. However, when applied to text generation from semi-structured data (e.g.,…
CREPE: Open-Domain Question Answering with False Presuppositions
Xinyan Velocity Yu, Sewon Min, Luke Zettlemoyer +1
Information seeking users often pose questions with false presuppositions, especially when asking about unfamiliar topics. Most existing question answering (QA) datasets, in contra…
Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources
Xinyan Velocity Yu, Akari Asai, Trina Chatterjee +2
While the NLP community is generally aware of resource disparities among languages, we lack research that quantifies the extent and types of such disparity. Prior surveys estimatin…
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…
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…