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Works31 from public data
- The rise and potential of large language model based agents: a survey914
A comprehensive survey on LLM-based agents is conducted, covering their construction frameworks, application scenarios, and the exploration of societies built upon LLM-based agents.
- InternLM2 Technical Report440
InternLM2 is introduced, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques.
- A Lexicon-Based Graph Neural Network for Chinese NER249
A lexicon-based graph neural network with global semantics is introduced, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency.
- Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training149
Supervised Contrastive Pre-training on large-scale sentiment-annotated corpora retrieved from in-domain language resources leads to better capture of both implicit and explicit sentiment orientation towards aspects in reviews, and comprehensive analysis validates its effectiveness on learning implicit sentiment.
- AgentGym: Evolving Large Language Model-based Agents across Diverse Environments113
This paper proposes AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration, and proposes a novel method, AgentEvol, to investigate the potential of agent self-evolution beyond previously seen data across tasks and environments.
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- EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models73
This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against LLMs, which supports 11 distinct jailbreak methods and facilitates the security validation of a broad spectrum of LLMs.
- Below Chance: Code and Derived Data for an Audit of the TRACE Continual-Learning Benchmark and a Census of Published Sequential Baselines70
TRACE is introduced, a novel benchmark designed to evaluate continual learning in LLMs and the Reasoning-augmented Continual Learning (RCL) approach, which integrates task-specific cues with meta-rationales, effectively reducing catastrophic forgetting in LLM while expediting convergence on novel tasks.
- Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling65
This work proposes a novel topic-augmented two-stage dialogue summarizer (TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues.
- AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments57
A GENT G YM is proposed, a framework featuring 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction that takes the initial step to develop LLM-based agents that can handle diverse tasks via methods like self-improvement or reinforcement learning.
- A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis45
A novel lexicon-based supervised attention model (LBSA) is proposed, which allows a recurrent neural network to focus on the sentiment content, thus generating sentiment-informative representations and has better interpretability and less noise.
- Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining35
This work exploits large-scale in-domain non-summary data to separately pretrain the dialogue encoder and the summary decoder and pretrained on the out-of-domain summary data using adversarial critics, aiming to facilitate domain-agnostic summarization.
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- Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems30
The proposed CG-nAR model comprises a multi-concept planning module that learns to identify multiple associated concepts from a concept graph and a customized Insertion Transformer that performs concept-guided non-autoregressive generation to complete a response.
- Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders30
Experimental results show that RankAE significantly outperforms other unsupervised methods and is able to generate high-quality summaries in terms of relevance and topic coverage.
- Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback28
Linear Alignment is introduced, a novel algorithm that aligns language models with human preferences in one single inference step, eliminating the reliance on data annotation and model training.
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- Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning6
This paper makes the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model and proposes to use contrastive learning to improve the quality of feature representations in a latent space for effective lifespan tissue segmentation.
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- NeuroVerse: Neuroimage Processing Tools Across a Century of Life1
This work presents robust automatic tools for accurate and consistent processing of neuroimaging data covering a century of life, and demonstrates their applicability to data covering the entire human lifespan.
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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 2026-10-15. 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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