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- Exploiting BERT for End-to-End Aspect-based Sentiment Analysis335
This work builds a series of simple yet insightful neural baselines to deal with E2E-ABSA, and shows that even with a simple linear classification layer, the BERT-based architecture can outperform state-of-the-art works.
- Aspect Sentiment Quad Prediction as Paraphrase Generation314
This work introduces the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, and proposes a novel Paraphrase modeling paradigm to cast the ASQP task to a paraphrase generation process.
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- Towards Generative Aspect-Based Sentiment Analysis306
This paper proposes to tackle various ABSA tasks in a unified generative framework with two types of paradigms, namely annotation-style and extraction-style modeling, to enable the training process by formulating each ABSA task as a text generation problem.
- Multilingual Jailbreak Challenges in Large Language Models283
Experimental results show that ChatGPT fine-tuned with such data can achieve a substantial reduction in unsafe content generation, and a novel self-defense framework that automatically generates multilingual training data for safety fine-tuning is proposed.
- M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models149
This work introduces M3Exam, a novel benchmark sourced from real and official human exam questions for evaluating LLMs in a multilingual, multimodal, and multilevel context, and finds that current models, including GPT-4, still struggle with multilingual text, particularly in low-resource and non-Latin script languages.
- Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching81
This paper considers the unsupervised cross-lingual transfer for the ABSA task, where only labeled data in the source language is available and aims at transferring its knowledge to the target language having no labeled data and proposes an alignment-free label projection method.
- A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning68
It is argued that the pretrained feature extractor itself can be strong enough to achieve a competitive or even better continual learning performance on Split-CIFAR100 and CoRe 50 benchmarks and a simple baseline is designed that achieves 88.53% on 10-Split-CifAR-100, surpassing most state-of-the-art continual learning methods that are all initialized using the same pretrained transformer model.
- Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems62
This paper investigates how a neural network understands patterns only from semantics, and proposes a contrastive learning approach, where the neural network perceives the divergence of patterns.
- Toward Personalized Answer Generation in E-Commerce via Multi-perspective Preference Modeling49
Experimental results on real-world E-commerce QA datasets demonstrate that the proposed Personalized Answer GEneration method outperforms existing methods by generating informative and customized answers and show that answer generation in E-Commerce can benefit from personalization.
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- Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling35
This paper proposes an answer ranking model named MUSE which carefully models multiple semantic relations among the question, answers, and relevant reviews, and achieves superior performance on the concerned answer ranking task.
- Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models34
A supervised learning framework called Bi-Factorial Preference Optimization is proposed, which re-parameterizes a joint RLHF objective of both safety and helpfulness into a single supervised learning objective and significantly outperforms existing approaches in both safety and helpfulness.
- Nonfactoid Question Answering as Query-Focused Summarization With Graph-Enhanced Multihop Inference34
A novel query-focused summarization method, namely a graph-enhanced multihop query- focused summarizer (GMQS), to tackle the nonfactoid QA problem and possesses the capability of performing explainable multihip reasoning.
- Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce33
Two kinds of opinion fusion strategies, namely, static and dynamic fusion, are proposed to distill and aggregate important opinion information learned from the opinion mining task into the answer generation process.
- The concept of decentralized and secure electronic marketplace33
The concept of Decentralized Electronic Marketplace (DEM) is proposed which allow buyers and sellers to engage in commercial transactions, subject to an explicitly stated set of trading rules, called the law of this marketplace—which they can trust to be observed by their trading partners.
- AnswerFact: Fact Checking in Product Question Answering32
This paper introduces AnswerFact, a large scale fact checking dataset from product question answering forums, and proposes a novel neural model with tailored evidence ranking components to handle the concerned answer veracity prediction problem.
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- Overcoming Generic Knowledge Loss with Selective Parameter Update30
This work proposes a novel approach that, instead of updating all parame-ters equally, localizes the updates to a sparse set of parame-ters relevant to the task being learned, striking a balance between efficiency and new task performance, while main-taining the transferability and generalizability of foundation models.
- Aspect-based Sentiment Analysis in Question Answering Forums30
A model with a design of cross-sentence aspect-opinion interaction modeling to address the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair.
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- Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers22
Babel is introduced, an open multilingual LLM that covers the top 25 languages by number of speakers, supports over 90% of the global population, and includes many languages neglected by other open multilingual LLMs and sets a new standard for open multilingual LLMs.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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