Yiming Yang

Learning Concept Graphs from Online Educational Data

2016 · Journal of Artificial Intelligence Research

This paper addresses an open challenge in educational data mining, i.e., the problem of automatically mapping online courses from different providers (universities, MOOCs, etc.) onto a universal space of concepts, and predicting latent prerequisite dependencies …

An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation

2019 · arXiv (Cornell University)

In this work, we propose a method for neural dialogue response generation that allows not only generating semantically reasonable responses according to the dialogue history, but also explicitly controlling the sentiment of the response via …

Pre-training Tasks for Embedding-based Large-scale Retrieval

2020 · arXiv (Cornell University)

We consider the large-scale query-document retrieval problem: given a query (e.g., a question), return the set of relevant documents (e.g., paragraphs containing the answer) from a large document corpus. This problem is often solved in …

EIGEN: Event Influence GENeration using Pre-trained Language Models

2020 · arXiv (Cornell University)

Reasoning about events and tracking their influences is fundamental to understanding processes. In this paper, we present EIGEN - a method to leverage pre-trained language models to generate event influences conditioned on a context, nature …

On the Sentence Embeddings from Pre-trained Language Models

2020 · arXiv (Cornell University)

Pre-trained contextual representations like BERT have achieved great success in natural language processing. However, the sentence embeddings from the pre-trained language models without fine-tuning have been found to poorly capture semantic meaning of sentences. In …

KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Exploiting Local and Global Features in Transformer-based Extreme Multi-label Text Classification

2022 · arXiv (Cornell University)

Extreme multi-label text classification (XMTC) is the task of tagging each document with the relevant labels from a very large space of predefined categories. Recently, large pre-trained Transformer models have made significant performance improvements in …

Optimizing Temperature for Language Models with Multi-Sample Inference

2025 · arXiv (Cornell University)

Multi-sample aggregation strategies, such as majority voting and best-of-N sampling, are widely used in contemporary large language models (LLMs) to enhance predictive accuracy across various tasks. A key challenge in this process is temperature selection, …

Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward

2025

Ruohong Zhang, Liangke Gui, Zhiqing Sun, Yihao Feng, Keyang Xu, Yuanhan Zhang, Di Fu, Chunyuan Li, Alexander G Hauptmann, Yonatan Bisk, Yiming Yang. Proceedings of the 2025 Conference of the Nations of the Americas Chapter …

Moving-Skewness Preprocessing for Simple Power Analysis on Cryptosystems: Revealing Asymmetry in Leakage

2026 · Cryptography

In side-channel analysis, simple power analysis (SPA) is a widely used technique for recovering secret information by exploiting differences between operations in traces. However, in realistic measurement environments, SPA is often hindered by noise, temporal …

RACE: Large-scale ReAding Comprehension Dataset From Examinations

2017 · arXiv (Cornell University)

We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to …

XLNet: Generalized Autoregressive Pretraining for Language Understanding

2019 · arXiv (Cornell University)

With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency …

Transformer-XL: Attentive Language Models beyond a Fixed-Length Context

2019

Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed …

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

2020

Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot …

Taming Pretrained Transformers for Extreme Multi-label Text Classification

2020

We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input text could be a product description on Amazon.com …

Self-Refine: Iterative Refinement with Self-Feedback

2023 · arXiv (Cornell University)

Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from …

Active Retrieval Augmented Generation

2023

Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.