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Works296 from public data
- Executable Code Actions Elicit Better LLM Agents699
This work proposes to use executable Python code to consolidate LLM agents' actions into a unified action space (CodeAct), and collects an instruction-tuning dataset CodeActInstruct, which can be used with existing data to improve models in agent-oriented tasks without compromising their general capability.
- Cross-lingual Name Tagging and Linking for 282 Languages595
This work develops a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia that is able to identify name mentions, assign a coarse-grained or fine- grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable.
- Overview of the TAC 2010 knowledge base population track583
An overview of the task definition and annotation challenges associated with KBP2010 is provided and the evaluation results and lessons that are learned are discussed based on detailed analysis.
- Efficient Attentions for Long Document Summarization488
Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source is proposed, able to process ten times more tokens than existing models that use full attentions.
- Tool Learning with Foundation Models471
A systematic investigation and comprehensive review of tool learning and discusses several open problems that require further investigation, such as ensuring trustworthy tool use, enabling tool creation with foundation models, and addressing personalization challenges.
- Large Language Models on Graphs: A Comprehensive Survey367
A systematic review of scenarios and techniques related to large language models on graphs, including LLM as Predictor, LLM as Encoder, and LLM as Aligner, and compare the advantages and disadvantages of different schools of models is provided.
- Training Software Engineering Agents and Verifiers with SWE-Gym360
This work presents SWE-Gym, the first environment for training real-world software engineering (SWE) agents, and uses SWE-Gym to train language model based SWE agents, achieving up to 19% absolute gains in resolve rate on the popular SWE-Bench Verified and Lite test sets.
- A Survey of Knowledge-enhanced Text Generation355
A comprehensive review of the research on knowledge-enhanced text generation over the past five years is presented, which includes two parts: (i) general methods and architectures for integrating knowledge into text generation; (ii) specific techniques and applications according to different forms of knowledge data.
- A Dependency-Based Neural Network for Relation Classification238
A new structure, termed augmented dependency path (ADP), is proposed, which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path, and a dependency-based neural networks (DepNN) are developed to exploit the semantic representation behind the ADP.
- Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries219
This work constructs a paired dataset of molecules and their corresponding text descriptions, which it uses to learn an aligned common semantic embedding space for retrieval and extends this to create a cross-modal attention-based model for explainability and reranking by interpreting the attentions as association rules.
- Text Classification Using Label Names Only: A Language Model Self-Training Approach205
This paper uses pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification, and achieves around 90% accuracy on four benchmark datasets.
- Artificial intelligence for science in quantum, atomistic, and continuum systems189
This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems, and provides an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations.
- Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners175
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction, and to demonstrate the power of language models in understanding videos on a wide variety of video- language tasks.
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- CLIP-Event: Connecting Text and Images with Event Structures159
A contrastive learning framework to enforce vision-language pretraining models to comprehend events and associated argument (participant) roles is proposed, which takes advantage of text information extraction technologies to obtain event structural knowledge, and utilizes multiple prompt functions to contrast difficult negative descriptions by manipulating event structures.
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- RM-R1: Reward Modeling as Reasoning149
It is hypothesized and validated that integrating reasoning into reward modeling significantly enhances RM's interpretability and performance, and a new class of generative reward models, Reasoning Reward Models (ReasRMs), are introduced, which formulate reward modeling as a reasoning task.
- Open Vocabulary Electroencephalography-to-Text Decoding and Zero-Shot Sentiment Classification146
It is proposed that the human brain functions as a special text encoder and a novel framework leveraging pre-trained language models (e.g., BART) is proposed, which achieves a 40.1% BLEU-1 score on EEG-To-Text decoding and a 55.6% F1 scoreon zero-shot EEG-based ternary sentiment classification, which significantly outperforms supervised baselines.
- Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks145
This work introduces Mobile-Agent-E, a hierarchical multi-agent framework capable of self-evolution through past experience capable of achieving 22% absolute improvement over previous state-of-the-art approaches across three foundation model backbones.
- ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning143
Experimental results demonstrate that ERICA can improve typical PLMs (BERT and RoBERTa) on several language understanding tasks, including relation extraction, entity typing and question answering, especially under low-resource settings.
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- If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents133
An overview of the various benefits of integrating code into LLMs' training data and how these profound capabilities of LLMs, brought by code, have led to their emergence as intelligent agents in situations where the ability to understand instructions, decompose goals, plan and execute actions, and refine from feedback are crucial to their success on downstream tasks are traced.
- COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation130
A novel and comprehensive knowledge discovery framework, COVID-KG, to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature and exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation.
- A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling117
A multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling that proves to be particularly effective for low-resource settings, when there are less than 200 training sentences for the target task.
- Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media117
This work proposes a graph based latent variable model that models the inter short text correlations (text-to-text information) and shows that using tweet specific feature and news specific feature as well as temporal constraints, it is able to extract text- to-text correlations, and thus completes the semantic picture of a short text.
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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