EMNLP

Don’t Forget Your Reward Values: Language Model Alignment via Value-based Calibration
While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), …
Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large Language Models
We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language …
Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths
Advanced models such as OpenAI o1 exhibit impressive problem-solving capabilities through step-by-step reasoning. However, they may …
Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning
In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has demonstrated high efficacy in several NLP …
Who’s Who: Large Language Models Meet Knowledge Conflicts in Practice
Retrieval-augmented generation (RAG) methods are viable solutions for addressing the static memory limits of pre-trained language …
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Prediction
Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those …
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources. The key idea is that that …
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning
To overcome the data sparsity issue in short text topic modeling, existing methods commonly rely on data augmentation or the data …
Textual Manifold-based Defense Against Natural Language Adversarial Examples
Despite the recent success of large pretrained language models in NLP, they are susceptible to adversarial examples. Concurrently, …
Enriching and Controlling Global Semantics for Text Summarization
Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative …