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- 660
- A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges127
This study aims to provide a comprehensive review for NLP researchers and legal professionals to understand the advances in LJP over the past years, and to facilitate further joint efforts towards improving the performance of LJP models.
- Improving Multi-turn Dialogue Modelling with Utterance ReWriter125
This paper proposes rewriting the human utterance as a pre-process to help multi-turn dialgoue modelling and introduces a Transformer-based utterance rewriting architecture using the pointer network, which achieves remarkably good performance on the utterance rewrite task.
- A Conditional Variational Framework for Dialog Generation120
This paper proposes a framework allowing conditional response generation based on specific attributes, which can be either manually assigned or automatically detected and validated on two different scenarios, where the attribute refers to genericness and sentiment states respectively.
- Improving Variational Encoder-Decoders in Dialogue Generation104
A separate VED model is developed that learns to autoencode discrete texts into continuous embeddings and generalize latent representations by reconstructing the encoded embedding using Gaussian noise and multi-layer perceptrons.
- Large Language Models Empowered Personalized Web Agents90
A Personalized User Memory-enhanced Alignment (PUMA) framework is proposed to adapt LLM-empowered personalized Web agents to adapt LLMs to the personalized Web agent task, and extensive experiments validate the superiority of PUMA over existing Web agents on PersonalWAB.
- LawBench: Benchmarking Legal Knowledge of Large Language Models89
Using LawBench, the first evaluation benchmark composed of 20 tasks aimed to assess the ability of Large Language Models (LLMs) to perform Chinese legal-related tasks, this work presents a comprehensive evaluation of 21 popular LLMs and the first comparative analysis of the empirical results.
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- State-of-health estimation based on real data of electric vehicles concerning user behavior80
A SOH estimation method based on real data of electric vehicles concerning user behavior and Bayesian optimization algorithm is used to ensure the generalization of the model when different electric vehicles produce different user behaviors.
- Logical Fallacy Detection70
It is found that existing pretrained large language models perform poorly on this task, and it is shown that a simple structure-aware classifier outperforms the best language model by 5.46% on Logic and 4.51% on logicClimate.
- EasyAug: An Automatic Textual Data Augmentation Platform for Classification Tasks59
EasyAug is a data augmentation platform that provides several augmentation approaches, such that with minimal effort a new method can comprehensively be compared with the baselines and can easily choose the most suitable one for their own dataset.
- On Training Instance Selection for Few-Shot Neural Text Generation52
This work proposes a simple selection strategy with K-means clustering in few-shot neural text generation, and shows that even with the naive clustering-based approach, the generation models consistently outperform random sampling on three text generation tasks: data-to-text generation, document summarization and question generation.
- Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence52
This work proposes to explicitly segment target text into fragment units and align them with their data correspondences to maintain the same expressive power as neural attention models, while being able to generate fully interpretable outputs with several times less computational cost.
- Diversifying Dialogue Generation with Non-Conversational Text48
This paper collects a large-scale non-conversational corpus from multi sources including forum comments, idioms and book snippets and presents a training paradigm to effectively incorporate these text via iterative back translation.
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- Neural Data-to-Text Generation with LM-based Text Augmentation46
This weakly supervised training paradigm is able to outperform fully supervised sequence-to-sequence models with less than 10% of the training set and can boost the performance of a standard sequence- to-sequence model by over 5 BLEU points, establishing a new state-of-the-art on both datasets.
- RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining44
RoCBert is a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation, synonyms, typos, etc, and is pretrained with the contrastive learning objective which maximizes the label consistency under different synthesized adversarial examples.
- Learning Fine-Grained Fact-Article Correspondence in Legal Cases38
This work creates a corpus with manually annotated fact-article correspondences and treats the learning as a text matching task and proposes a multi-level matching network to address it, which can improve the recommendation accuracy by a large margin.
- CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs37
This work introduces a visual preference optimization module within the DPO framework, enabling MLLMs to learn from both textual and visual preferences simultaneously, and proposes a hierarchical textual preference optimization module that allows the model to capture preferences at multiple granular levels, including response, segment, and token levels.
- Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices36
This paper systematically investigates two core aspects of multi-layer visual feature fusion, selecting the most effective visual layers and identifying the best fusion approach with the language model.
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- AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarization36
Experiments show that AST-Transformer outperforms the state-of-arts by a substantial margin while being able to reduce 90 ~ 95% of the computational complexity in the encoding process.
- MovieChats: Chat like Humans in a Closed Domain34
This work takes a close look at the movie domain and presents a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.
- Estimation of Gap Between Current Language Models and Human Performance32
Evaluating LMs in a generative setting and running a human evaluation on the generated sentences imply that LMs need about 10 to 20 more years of research before human performance is reached.
- Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator31
This paper allows the model to “edit” pointed tokens instead of always hard copying them, and shows the model is able to capture more latent alignment relations than exact word matches, improve word alignment accuracy, and generate higher-quality summaries validated by both qualitative and quantitative evaluations.
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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