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- DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services183
Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of the system in serving various users across diverse legal scenarios, and enhances models' ability to access and utilize external legal knowledge.
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- Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text94
The results show that the proposed logic-driven data augmentation algorithm achieves state-of-the-art performance on both datasets, and even surpasses human performance on the ReClor dataset.
- DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning85
This work proposes Multiple Experts Fine-tuning Framework, a methodology that improves general LLMs by endowing them with multi-turn question answering abilities, domain text processing capabilities, mathematical computation skills, and retrieval-enhanced generation capabilities.
- Mask Attention Networks: Rethinking and Strengthen Transformer83
A novel understanding of SAN and FFN are presented as Mask Attention Networks (MANs) and it is shown that they are two special cases of MANs with static mask matrices, which limit the capability for localness modeling in text representation learning.
- Automatic Summarization of Legal Decisions using Iterative Masking of Predictive Sentences63
It is shown that sentence predictiveness does not reliably cover all decision-relevant aspects of a case, illustrate that lexical overlap metrics are not well suited for evaluating legal summaries, and suggest that future work should focus on case-aspect coverage.
- A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators51
This work argues that natural questions need to have two specific attributes from the perspectives of content and linguistic respectively, namely, natural and human-written, and proposes two discriminators, one for each attribute, to enhance the training.
- A Multi-Agent Communication Framework for Question-Worthy Phrase Extraction and Question Generation46
A multi-agent communication framework is introduced, taking phrase extraction and question generation as two agents, and learn these two tasks simultaneously via message passing mechanism and can extract question-worthy phrases, which are able to improve the performance of question generation.
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- An Enhanced Knowledge Injection Model for Commonsense Generation32
This work integrates two additional modules into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure and shows results that significantly improves the performance on all the metrics.
- Analytical Reasoning of Text31
This paper studies the challenge of analytical reasoning of text and collects a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016, and presents an approach dubbed ARM, which outperforms pre-trained models significantly.
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- TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning30
A Theme Concepts extended Image Captioning (TCIC) framework that incorporates theme concepts to represent high-level cross-modality semantics is proposed and experimental results on MS COCO show the effectiveness of the approach compared to some state-of-the-art models.
- Task-Agnostic Structured Pruning of Speech Representation Models29
A fine-grained attention head pruning method that can achieve comparable performance to the dense model in multiple tasks and outperforms the Wav2vec 2.0 base model on average, with 72% fewer parameters and 2 times faster inference speed.
- Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning27
A novel hierarchical structure is proposed to construct an image-grounded vocabulary, based on which, captions are generated with limitation and guidance, and the effectiveness of this framework compared to some state-of-the-art models is shown.
- PathQG: Neural Question Generation from Facts22
This paper presents a novel task of question generation given a query path in the knowledge graph constructed from the input text, and formulate query representation learning as a sequence labeling problem for identifying the involved facts to form a query and employ an RNN-based generator for question generation.
- In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search17
This work introduces Logic-Induced-Knowledge-Search (LINK), a systematic long-tail data generation framework, and uses LINK to curate Logic-Induced-Long-Tail (LINT), a large-scale long-tail inferential knowledge dataset that contains 108K statements spanning four domains.
- Constructing Phrase-level Semantic Labels to Form Multi-Grained Supervision for Image-Text Retrieval14
This paper proposes Semantic Structure Aware Multimodal Transformer (SSAMT) for multi-modal representation learning, and utilizes different kinds of attention mechanisms to enforce interactions of multi-grained semantic units in both sides of vision and language.
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- Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering11
This paper proposes an interpretable stepwise reasoning framework to incorporate both single-hop supporting sentence identification and single-Hop question generation at each intermediate step, and utilize the inference of the current hop for the next until reasoning out the final result.
- Smart Flexible Tactile Sensors: Recent Progress in Device Designs, Intelligent Algorithms, and Multidisciplinary Applications10
A critical focus of this analysis is on how artificial‐intelligence‐driven algorithms assist sensors in overcoming key challenges such as noise suppression, feature extraction, and environmental adaptability, leading to significantly enhanced perception accuracy and decision‐making autonomy in dynamic scenarios.
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- Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning8
This model identifies four types of elementary knowledge structures from contexts to construct structured queries, and utilizes the box embedding method to conduct explicit structure reasoning along queries during language modeling.
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- Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval8
This work argues that current retrieval-based negative sample construction approach is limited in the scale of the dataset thus fail to identify negative sample of high difficulty for every image, and proposes TAGS-DC to generate synthetic sentences automatically as negative samples.
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- A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training6
Taking the open-domain uni-modal datasets of images and texts as input, a knowledge graph is maintained as the foundation to support knowledge discovery and model pre-training in a continuous learning framework for mutual improvement.
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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 2026-10-16. 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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