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- Attention-based LSTM for Aspect-level Sentiment Classification2,397
This paper reveals that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect, and proposes an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification.
- ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools1,810
This report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B, which represent the most capable models that are trained with all the insights and lessons gained from the preceding three generations of ChatGLM.
- AgentBench: Evaluating LLMs as Agents1,460
AgentBench is presented, a multi-dimensional benchmark that consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities and shows that poor long-term reasoning, decision-making, and instruction following abilities are the main obstacles for developing usable LLM agents.
- Pre-trained models: Past, present and future1,134
A deep look into the history of pre-training, especially its special relation with transfer learning and self-supervised learning, is taken to reveal the crucial position of PTMs in the AI development spectrum.
- Large Language Models Are Not Robust Multiple Choice Selectors620
This work proposes a label-free, inference-time debiasing method, called PriDe, which separates the model's prior bias for option IDs from the overall prediction distribution, and demonstrates that it achieves interpretable and transferable debiasing with high computational efficiency.
- Towards Emotional Support Dialog Systems535
The Emotional Support Conversation task is defined and an ESC Framework is proposed, which is grounded on the Helping Skills Theory, to show the importance of support strategies in providing effective emotional support and the utility of ESConv in training more emotional support systems.
- Commonsense Knowledge Aware Conversation Generation with Graph Attention525
This is the first attempt that uses large-scale commonsense knowledge in conversation generation, and unlike existing models that use knowledge triples (entities) separately and independently, this model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs.
- GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models474
GLM-4.5 is presented, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes that ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks.
- Learning to identify review spam440
This paper exploits machine learning methods to identify review spam and provides a twoview semi-supervised method, co-training, to exploit the large amount of unlabeled data and shows that the proposed method is effective.
- TransG : A Generative Model for Knowledge Graph Embedding369
This paper addresses a new issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples, and proposes a novel Gaussian mixture model for embedding, TransG, which can discover latent semantics for a relation and leverage a mixture of relation component vectors for embeddedding a fact triple.
- Structure-Aware Review Mining and Summarization362
This paper proposes a new machine learning framework based on Conditional Random Fields that can employ rich features to jointly extract positive opinions, negative opinions and object features for review sentences and shows that structure-aware models outperform many state-of-the-art approaches to review mining.
- Challenges in Building Intelligent Open-domain Dialog Systems359
This article reviews the recent work on neural approaches that are devoted to addressing three challenges in developing intelligent open-domain dialog systems: semantics, consistency, and interactiveness.
- Reinforcement Learning for Relation Classification From Noisy Data355
Experiment results show that the proposed novel model can deal with the noise of data effectively and obtains better performance for relation classification at the sentence level.
- ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving331
This paper proposes ToRA a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical problems by seamlessly integrating natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of tools.
- Agent-SafetyBench: Evaluating the Safety of LLM Agents311
This paper introduces Agent-SafetyBench, a comprehensive benchmark designed to evaluate the safety of LLM agents, and summarizes two fundamental safety defects in current LLM agents: lack of robustness and lack of risk awareness.
- Augmenting End-to-End Dialogue Systems With Commonsense Knowledge282
This model represents the first attempt to integrating a large commonsense knowledge base into end-to-end conversational models and suggests that the knowledge-augmented models are superior to their knowledge-free counterparts.
- A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation269
A knowledge-enhanced pretraining model to utilize commonsense knowledge from external knowledge bases to generate reasonable stories that can generate more reasonable stories than state-of-the-art baselines, particularly in terms of logic and global coherence.
- A Hierarchical Framework for Relation Extraction with Reinforcement Learning253
This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation and applies a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types.
- Sentiment Analysis with Global Topics and Local Dependency250
This paper proposes a major departure from the previous approaches by assuming that the sentiments are related to the topic in the document, and puts forward a joint sentiment and topic model, i.e. Sentiment-LDA, and shows that exploiting the sentiment dependency is clearly advantageous, and that the Dependency-Sentiment- LDA is an effective approach for sentiment analysis.
- Discovering patterns to extract protein–protein interactions from full texts248
A robust and powerful methodology to mine protein-protein interactions from biomedical texts by using a dynamic programming algorithm to compute distinguishing patterns by aligning relevant sentences and key verbs that describe protein interactions.
- SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions196
This paper proposes the semantic space projection (SSP) model, a model which jointly learns from the symbolic triples and textual descriptions to discover semantic relevance and offer precise semantic embedding.
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- COLD: A Benchmark for Chinese Offensive Language Detection183
The factors that influence the offensive generations are investigated, and it is found that anti-bias contents and keywords referring to certain groups or revealing negative attitudes trigger offensive outputs easier.
- The gene normalization task in BioCreative III182
Evaluating teams using the gold standard and inferred ground truth shows that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible, and shows measures of comparative performance between teams.
- Densely Connected CNN with Multi-scale Feature Attention for Text Classification180
A densely connected CNN with multi-scale feature attention with competitive performance against state-of-the-art baselines on five benchmark datasets and reveals the model's ability to select proper n-gram features for text classification.
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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