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Works54 from public data
- Learning to Ask: Neural Question Generation for Reading Comprehension763
An attention-based sequence learning model for the task and the effect of encoding sentence- vs. paragraph-level information is investigated and results show that the system significantly outperforms the state-of-the-art rule-based system.
- Event Extraction by Answering (Almost) Natural Questions505
This work introduces a new paradigm for event extraction by formulating it as a question answering (QA) task, which extracts the event arguments in an end-to-end manner and outperforms prior methods substantially.
- Harvesting Paragraph-level Question-Answer Pairs from Wikipedia178
It is found that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art.
- PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations146
The peer rank (PR) algorithm that takes into account each peer LLM's pairwise preferences of all answer pairs, and outputs a final ranking of models; and peer discussion (PD), where two LLMs are prompt to discuss and try to reach a mutual agreement on the preferences of two answers, are proposed.
- Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding119
This work proposes a novel multi-granularity reader to dynamically aggregate information captured by neural representations learned at different levels of granularity, and evaluates the models on the MUC-4 event extraction dataset, showing that the best system performs substantially better than prior work.
- LLM4SR: A Survey on Large Language Models for Scientific Research106
This paper presents the first systematic survey dedicated to exploring how LLMs are revolutionizing the scientific research process, and analyzes the unique roles LLMs play across four critical stages of research: hypothesis discovery, experiment planning and implementation, scientific writing, and peer reviewing.
- Identifying Where to Focus in Reading Comprehension for Neural Question Generation102
A hierarchical neural sentence-level sequence tagging model that is fully data-driven — with no sophisticated NLP pipelines or any hand-crafted rules/features — and compares favorably to a number of baselines when evaluated on the SQuAD data set.
- GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction91
A generative transformer-based encoder-decoder framework (GRIT) that is designed to model context at the document level: it can make extraction decisions across sentence boundaries; is implicitly aware of noun phrase coreference structure, and has the capacity to respect cross-role dependencies in the template structure.
- MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents71
MLR-COPILOT, an autonomous Machine Learning Research framework powered by large language model agents, is presented, designed to enhance ML research productivity through automatic generation and implementation of research ideas within constraints.
- Few-shot Intent Classification and Slot Filling with Retrieved Examples62
This paper proposes a span-level retrieval method that learns similar contextualized representations for spans with the same label via a novel batch-softmax objective and uses the labels of the retrieved spans to construct the final structure with the highest aggregated score.
- FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models55
A reference-free and fine-grained evaluation metric that measures the faithfulness of the generated free-form answers from large vision-language models (LVLMs), which reveals that current systems are prone to generate hallucinated content unfaithful to the image, which leaves room for future improvements.
- Dynamic Global Memory for Document-level Argument Extraction48
A new global neural generation-based framework for document-level event argument extraction is introduced by constructing a document memory store to record the contextual event information and leveraging it to implicitly and explicitly help with decoding of arguments for later events.
- Template Filling with Generative Transformers45
Experiments demonstrate that this framework substantially outperforms pipeline-based approaches, and other neural end-to-end baselines that do not model between-event dependencies, and improves performance on documents containing multiple events.
- Logical Reasoning over Natural Language as Knowledge Representation: A Survey41
This paper provides a comprehensive overview on a new paradigm of logical reasoning, which uses natural language as knowledge representation and pretrained language models as reasoners, including philosophical definition and categorization of logical reasoning.
- FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback39
This work proposes an innovative method to align modalities in LVLMs through Fine-Grained Artificial Intelligence Feedback (FGAIF), which mainly consists of three steps: AI-based Feedback Collection, Fine-grained Reward Model Training, and Reinforcement Learning with Fine-grained Reward.
- RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios39
This work introduces RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios and builds a demo website based on the dockerized system and schema library.
- QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining34
This work introduces QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model, and uses a linguistically motivated questioning strategy to turn descriptions into questions, allowing the model to generalize to unseen slot types.
- MEQA: A Benchmark for Multi-hop Event-centric Question Answering with Explanations33
A novel semi-automatic question generation strategy by composing event structures from information extraction datasets and the first Multi-hop Event-centric Question Answering (MEQA) benchmark is presented, which shows that MEQA is challenging for the latest state-of-the-art models encompassing large language models (LLMs); and how they fall short of providing faithful explanations of the event-centric reasoning process.
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- Be Consistent! Improving Procedural Text Comprehension using Label Consistency31
This work presents a new learning framework that leverages label consistency during training, allowing consistency bias to be built into the model and significantly improves prediction performance over prior state-of-the-art systems.
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- Improving Event Duration Prediction via Time-aware Pre-training24
This work introduces two effective models for duration prediction, which incorporate external knowledge by reading temporal-related news sentences (time-aware pre-training), and demonstrates these models are capable of duration prediction in the unsupervised setting, outperforming the baselines.
- Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning23
Empirical results demonstrate that the proposed Logical Entity RePresentation (LERP) model outperforms other rule learning methods in knowledge graph completion and is comparable or even superior to state-of-the-art black-box methods.
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- LDC: Learning to Generate Research Idea with Dynamic Control21
A novel framework that employs a two-stage approach combining Supervised Fine-Tuning and controllable Reinforcement Learning to provide a balanced approach to research ideation, achieving high-quality outcomes by dynamically navigating the trade-offs among novelty, feasibility, and effectiveness.
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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