Lidong Bing

Learning Domain-Sensitive and Sentiment-Aware Word Embeddings

2018

Word embeddings have been widely used in sentiment classification because of their efficacy for semantic representations of words. Given reviews from different domains, some existing methods for word embeddings exploit sentiment information, but they cannot …

Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

2019 · arXiv (Cornell University)

For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies. For example few-shot learning paradigm for relations has been investigated. In …

Enhancing Cross-lingual Prompting with Dual Prompt Augmentation

2023

Prompting shows promising results in few-shot scenarios. However, its strength for multilingual/cross-lingual problems has not been fully exploited. hao and Schütze (2021) made initial explorations in this direction by presenting that cross-lingual prompting outperforms cross-lingual …

A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization

2023

Pre-trained language models (PLMs) have achieved outstanding achievements in abstractive single-document summarization (SDS). However, such benefits may not fully extend to multi-document summarization (MDS), where the handling of cross-document information is more complex. Previous works …

Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models

2024 · arXiv (Cornell University)

Large language models (LLMs) have demonstrated impressive reasoning capabilities, particularly in textual mathematical problem-solving. However, existing open-source image instruction fine-tuning datasets, containing limited question-answer pairs per image, do not fully exploit visual information to enhance …

AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation

2024 · arXiv (Cornell University)

The impressive performance of proprietary LLMs like GPT4 in code generation has led to a trend to replicate these capabilities in open-source models through knowledge distillation (e.g. Code Evol-Instruct). However, these efforts often neglect the …

AdaMergeX: Cross-Lingual Transfer with Large Language Models via Adaptive Adapter Merging

2025

Yiran Zhao, Wenxuan Zhang, Huiming Wang, Kenji Kawaguchi, Lidong Bing. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). …

Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

We propose a new unsupervised sentence salience framework for Multi-Document Summarization (MDS), which can be divided into two components: latent semantic modeling and salience estimation. For latent semantic modeling, a neural generative model called Variational …

Neural Rating Regression with Abstractive Tips Generation for Recommendation

2017

Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In …

Generating Distractors for Reading Comprehension Questions from Real Examinations

2018 · arXiv (Cornell University)

We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations. In contrast to all previous works, we do not aim at preparing words or short phrases distractors, instead, we endeavor …

Difficulty Controllable Generation of Reading Comprehension Questions

2019

We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG). Taking as input a sentence in the reading comprehension …

Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation

2022 · Findings of the Association for Computational Linguistics: ACL 2022

Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once. In this paper, we propose a semi-supervised framework for DocRE with …

Is GPT-3 a Good Data Annotator?

2023

Bosheng Ding, Chengwei Qin, Linlin Liu, Yew Ken Chia, Boyang Li, Shafiq Joty, Lidong Bing. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

2023

The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e.g., ChatDoctor) or …