147 citations · 1k across the 56 of their papers we have counts for
47 papers · 1 filter
Reinforcement Learning Enhanced LLMs: A Survey
Shuhe Wang, Shengyu Zhang, Jie Zhang +7
Reinforcement learning (RL) enhanced large language models (LLMs), particularly exemplified by DeepSeek-R1, have exhibited outstanding performance. Despite the effectiveness in imp…
Sim-GPT: Text Similarity via GPT Annotated Data
Shuhe Wang, Beiming Cao, Shengyu Zhang +5
Due to the lack of a large collection of high-quality labeled sentence pairs with textual similarity scores, existing approaches for Semantic Textual Similarity (STS) mostly rely o…
Sentiment Analysis through LLM Negotiations
Xiaofei Sun, Xiaoya Li, Shengyu Zhang +5
A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning. This framework suffers…
Instruction Tuning for Large Language Models: A Survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li +8
This paper surveys research works in the quickly advancing field of instruction tuning (IT), which can also be referred to as supervised fine-tuning (SFT)\footnote{In this paper, u…
Pushing the Limits of ChatGPT on NLP Tasks
Xiaofei Sun, Linfeng Dong, Xiaoya Li +8
Despite the success of ChatGPT, its performances on most NLP tasks are still well below the supervised baselines. In this work, we looked into the causes, and discovered that its s…
Text Classification via Large Language Models
Xiaofei Sun, Xiaoya Li, Jiwei Li +4
Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3, their performances still significantly underperform fine-tuned models in the task of text classi…
GPT-NER: Named Entity Recognition via Large Language Models
Shuhe Wang, Xiaofei Sun, Xiaoya Li +5
Despite the fact that large-scale Language Models (LLM) have achieved SOTA performances on a variety of NLP tasks, its performance on NER is still significantly below supervised ba…
Backdoor Attacks with Input-unique Triggers in NLP
Xukun Zhou, Jiwei Li, Tianwei Zhang +3
Backdoor attack aims at inducing neural models to make incorrect predictions for poison data while keeping predictions on the clean dataset unchanged, which creates a considerable…
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
Shuhe Wang, Yuxian Meng, Rongbin Ouyang +4
To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL mode…
NN-NER: Named Entity Recognition with Nearest Neighbor Search
Shuhe Wang, Xiaoya Li, Yuxian Meng +4
Inspired by recent advances in retrieval augmented methods in NLP~\citep{khandelwal2019generalization,khandelwal2020nearest,meng2021gnn}, in this paper, we introduce a nearest…
Faster Nearest Neighbor Machine Translation
Shuhe Wang, Jiwei Li, Yuxian Meng +5
NN based neural machine translation (NN-MT) has achieved state-of-the-art results in a variety of MT tasks. One significant shortcoming of NN-MT lies in its inefficiency i…
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
Leilei Gan, Jiwei Li, Tianwei Zhang +6
Backdoor attacks pose a new threat to NLP models. A standard strategy to construct poisoned data in backdoor attacks is to insert triggers (e.g., rare words) into selected sentence…
Interpreting Deep Learning Models in Natural Language Processing: A Review
Xiaofei Sun, Diyi Yang, Xiaoya Li +6
Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks. However, a long-standing criticism against neural netw…
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
Kangjie Chen, Yuxian Meng, Xiaofei Sun +4
Pre-trained Natural Language Processing (NLP) models can be easily adapted to a variety of downstream language tasks. This significantly accelerates the development of language mod…
GNN-LM: Language Modeling based on Global Contexts via GNN
Yuxian Meng, Shi Zong, Xiaoya Li +4
Inspired by the notion that ``{\it to copy is easier than to memorize}``, in this work, we introduce GNN-LM, which extends the vanilla neural language model (LM) by allowing to ref…
OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts
Shuhe Wang, Yuxian Meng, Xiaoya Li +3
In order to better simulate the real human conversation process, models need to generate dialogue utterances based on not only preceding textual contexts but also visual contexts.…
An MRC Framework for Semantic Role Labeling
Nan Wang, Jiwei Li, Yuxian Meng +5
Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument label…
Paraphrase Generation as Unsupervised Machine Translation
Xiaofei Sun, Yufei Tian, Yuxian Meng +4
In this paper, we propose a new paradigm for paraphrase generation by treating the task as unsupervised machine translation (UMT) based on the assumption that there must be pairs o…
ConRPG: Paraphrase Generation using Contexts as Regularizer
Yuxian Meng, Xiang Ao, Qing He +5
A long-standing issue with paraphrase generation is how to obtain reliable supervision signals. In this paper, we propose an unsupervised paradigm for paraphrase generation based o…
Layer-wise Model Pruning based on Mutual Information
Chun Fan, Jiwei Li, Xiang Ao +3
The proposed pruning strategy offers merits over weight-based pruning techniques: (1) it avoids irregular memory access since representations and matrices can be squeezed into thei…
Folden: -Fold Ensemble for Out-Of-Distribution Detection
Xiaoya Li, Jiwei Li, Xiaofei Sun +5
Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP). In this work, we propose a simple yet effective framework Folden, which mimics…
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
Zijun Sun, Xiaoya Li, Xiaofei Sun +6
Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for l…
Defending Against Backdoor Attacks in Natural Language Generation
Xiaofei Sun, Xiaoya Li, Yuxian Meng +4
The frustratingly fragile nature of neural network models make current natural language generation (NLG) systems prone to backdoor attacks and generate malicious sequences that cou…
Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation
Shuhe Wang, Yuxian Meng, Xiaofei Sun +5
Multi-modal dialog modeling is of growing interest. In this work, we propose frameworks to resolve a specific case of multi-modal dialog generation that better mimics multi-modal d…