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Works458 from public data
- Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning1,670
This paper proposes a novel adaptive attention model with a visual sentinel that sets the new state-of-the-art by a significant margin on image captioning.
- Large language models generate functional protein sequences across diverse families1,189
ProGen is described, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics.
- 1,137
- SummEval: Re-evaluating Summarization Evaluation1,112
This work re-evaluate 14 automatic evaluation metrics in a comprehensive and consistent fashion using neural summarization model outputs along with expert and crowd-sourced human annotations and implements and shares a toolkit that provides an extensible and unified API for evaluating summarization models across a broad range of automatic metrics.
- Evaluating the Factual Consistency of Abstractive Text Summarization1,066
A weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary substantially outperforms previous models, including those trained with strong supervision using standard datasets for natural language inference and fact checking.
- ERASER: A Benchmark to Evaluate Rationalized NLP Models957
This work proposes the Evaluating Rationales And Simple English Reasoning (ERASER) a benchmark to advance research on interpretable models in NLP, and proposes several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are.
- Diffusion Model Alignment Using Direct Preference Optimization923
This work proposes Diffusion-DPO, a method to align diffusion models to human preferences by directly optimizing on human comparison data, and fine-tuned the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1.0 model with Diffusion-DPO.
- 837
- Unified Training of Universal Time Series Forecasting Transformers831
This work presents novel enhancements to the conventional time series Transformer architecture, resulting in the proposed Masked Encoder-based Universal Time Series Forecasting Transformer (Moirai), which achieves competitive or superior performance when compared to full-shot models.
- 731
- 730
- Explain Yourself! Leveraging Language Models for Commonsense Reasoning663
This work collects human explanations for commonsense reasoning in the form of natural language sequences and highlighted annotations in a new dataset called Common Sense Explanations to train language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation framework.
- End-to-End Dense Video Captioning with Masked Transformer612
This work proposes an end-to-end transformer model, which employs a self-attention mechanism, which enables the use of efficient non-recurrent structure during encoding and leads to performance improvements.
- 603
- A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks589
A joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks and uses a simple regularization term to allow for optimizing all model weights to improve one task’s loss without exhibiting catastrophic interference of the other tasks.
- A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT556
This survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, cross-domain learning ability, and user-friendly interactive ability for artificial general intelligence.
- Intent Contrastive Learning for Sequential Recommendation509
Intent Contrastive Learning (ICL) is proposed, a general learning paradigm that leverages a latent variable to represent users’ intents and learn the distribution function of the latent variable via clustering in SR models via contrastive SSL.
- Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems497
A Transferable Dialogue State Generator (TRADE) that generates dialogue states from utterances using copy mechanism, facilitating transfer when predicting (domain, slot, value) triplets not encountered during training.
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- ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding443
ULIP is introduced to learn a unified representation of image, text, and 3D point cloud by pre-training with object triplets from the three modalities, using a pre-trained vision-language model that has already learned a common visual and textual space by training with massive image-text pairs.
- 428
- Multi-Hop Knowledge Graph Reasoning with Reward Shaping418
This work reduces the impact of false negative supervision by adopting a pretrained one-hop embedding model to estimate the reward of unobserved facts and counter the sensitivity to spurious paths of on-policy RL by forcing the agent to explore a diverse set of paths using randomly generated edge masks.
- Neural Text Summarization: A Critical Evaluation413
This work critically evaluate key ingredients of the current research setup: datasets, evaluation metrics, and models, and highlights three primary shortcomings: automatically collected datasets leave the task underconstrained and may contain noise detrimental to training and evaluation.
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 2026-10-14. 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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