Bing Qin

Exploring Segment Representations for Neural Segmentation Models

2016 · arXiv (Cornell University)

Many natural language processing (NLP) tasks can be generalized into segmentation problem. In this paper, we combine semi-CRF with neural network to solve NLP segmentation tasks. Our model represents a segment both by composing the …

Distilling Knowledge for Search-based Structured Prediction

2018 · arXiv (Cornell University)

Many natural language processing tasks can be modeled into structured prediction and solved as a search problem. In this paper, we distill an ensemble of multiple models trained with different initialization into a single model. …

Topic-to-Essay Generation with Neural Networks

2018

We focus on essay generation, which is a challenging task that generates a paragraph-level text with multiple topics.Progress towards understanding different topics and expressing diversity in this task requires more powerful generators and richer training …

e-CARE: a New Dataset for Exploring Explainable Causal Reasoning

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

Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal facts to facilitate the causal reasoning process. …

MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization

2022

Dialogue summarization helps users capture salient information from various types of dialogues has received much attention recently. However, current works mainly focus on English dialogue summarization, leaving other languages less well explored. Therefore, we present …

Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications

2023 · arXiv (Cornell University)

Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these challenges, researchers have pursued two …

Aligning Translation-Specific Understanding to General Understanding in Large Language Models

2024 · arXiv (Cornell University)

Large Language models (LLMs) have exhibited remarkable abilities in understanding complex texts, offering a promising path towards human-like translation performance. However, this study reveals the misalignment between the translation-specific understanding and the general understanding inside …

CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models

2024 · arXiv (Cornell University)

Cognitive dynamics are pivotal to advance human understanding of the world. Recent advancements in large language models (LLMs) reveal their potential for cognitive simulation. However, these LLM-based cognitive studies primarily focus on static modeling, overlooking …

Towards Comprehensive Post Safety Alignment of Large Language Models via Safety Patching

2024 · arXiv (Cornell University)

Safety alignment of large language models (LLMs) has been gaining increasing attention. However, current safety-aligned LLMs suffer from the fragile and imbalanced safety mechanisms, which can still be induced to generate unsafe responses, exhibit over-safety …

Final: Combining First-Order Logic With Natural Logic for Question Answering

2025 · IEEE Transactions on Knowledge and Data Engineering

Many question-answering problems can be approached as textual entailment tasks, where the hypotheses are formed by the question and candidate answers, and the premises are derived from an external knowledge base. However, current neural methods …

FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging

2025 · arXiv (Cornell University)

With the development of large language models, fine-tuning has emerged as an effective method to enhance performance in specific scenarios by injecting domain-specific knowledge. In this context, model merging techniques provide a solution for fusing …

Hierarchical Attention Flow for Multiple-Choice Reading Comprehension

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

In this paper, we focus on multiple-choice reading comprehension which aims to answer a question given a passage and multiple candidate options. We present the hierarchical attention flow to adequately leverage candidate options to model …

Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer

2018

Neural networks have been widely used for high resource language (e.g. English) named entity recognition (NER) and have shown state-of-the-art results.However, for low resource languages, such as Dutch, Spanish, due to the limitation of resources …

Revisiting Pre-Trained Models for Chinese Natural Language Processing

2020

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pretrained language models. In this paper, we target …

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

2020

Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou. Findings of the Association for Computational Linguistics: EMNLP 2020. 2020.

Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization

2021

Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, Ting Liu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long …