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- MMMU: A Massive Multi-Discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI2,743
The introduction of MMMU, a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning, will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence.
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- FinQA: A Dataset of Numerical Reasoning over Financial Data890
A new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts, aiming to automate the analysis of a large corpus of financial documents, and demonstrates that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge.
- MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing770
This work introduces MagicBrush, the first large-scale, manually annotated dataset for instruction-guided real image editing that covers diverse scenarios: single-turn, multi- turn, mask-provided, and mask-free editing, and fine-tune InstructPix2Pix on it and shows that the new model can produce much better images according to human evaluation.
- HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data452
HybridQA is presented, a new large-scale question-answering dataset that requires reasoning on heterogeneous information and can serve as a challenging benchmark to study question answering withheterogeneous information.
- Long-context LLMs Struggle with Long In-context Learning402
A benchmark for long in-context learning in extreme-label classification using six datasets with 28 to 174 classes and input lengths from 2K to 50K tokens is introduced and reveals that long context understanding and reasoning is still a challenging task for the existing LLMs.
- MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training394
An acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training, which outperforms conventional speech and audio approaches in terms of performance.
- VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks279
It is shown that VLMs are secretly strong embedding models that achieves an absolute average improvement of 10% to 20% over existing multimodal embedding models on both in-distribution and out-of-distribution datasets in MMEB.
- Re-Imagen: Retrieval-Augmented Text-to-Image Generator272
The Retrieval-Augmented Text-to-Image Generator (Re-Imagen), a generative model that uses retrieved information to produce high-fidelity and faithful images, even for rare or unseen entities, is presented.
- Video Captioning via Hierarchical Reinforcement Learning262
A novel hierarchical reinforcement learning framework for video captioning, where a high-level Manager module learns to design sub-goals and a low-level Worker module recognizes the primitive actions to fulfill the sub-goal.
- MANTIS: Interleaved Multi-Image Instruction Tuning257
This paper meticulously construct Mantis-Instruct containing 721K multi-image instruction data to train a family of Mantis models, which empowers Mantis with different multi-image skills like co-reference, comparison, reasoning, and temporal understanding, and shows its generalization ability.
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- Logical Natural Language Generation from Open-Domain Tables187
A new NLG task where a model is tasked with generating natural language statements that can be logically entailed by the facts in an open-domain semi-structured table is suggested, and new automatic metrics to evaluate the fidelity of generation models w.r.t. logical inference are proposed.
- Explanations from Large Language Models Make Small Reasoners Better181
This paper considers the problem of leveraging the explanations generated by LLM to improve the training of small reasoners, which are more favorable in real-production deployment due to their low cost.
- No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling165
Though automatic evaluation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that this approach achieves significant improvement in generating more human-like stories than SOTA systems.
- OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision161
This paper presents \omniedit, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly, and proposes a new editing architecture called EditNet to greatly boost the editing success rate.
- ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation159
This work proposes ConsistI2V, a diffusion-based method to enhance visual consistency for I2V generation that introduces spatiotemporal attention over the first frame to maintain spatial and motion consistency and noise initialization from the low-frequency band of the first frame to enhance layout consistency.
- LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs157
A new framework LongRAG, consisting of a `long retriever' and a `long reader' and a `long reader', is proposed, which processes the entire Wikipedia corpus into 4K-token units by grouping related documents by grouping related documents.
- Few-shot In-context Learning for Knowledge Base Question Answering156
This work proposes KB-BINDER, which for the first time enables few-shot in- context learning over KBQA tasks and can achieve a strong performance with only a few in-context demonstrations.
- Few-Shot NLG with Pre-Trained Language Model150
This work proposes the new task of few-shot natural language generation and proposes a simple yet effective approach that achieves very reasonable performances and outperforms the strongest baseline by an average of over 8.0 BLEU points improvement.
- MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions144
The core thesis of this paper is that text instructions can enable retrieving images with richer relations beyond visual similarity, and introduces MagicLens, a series of self-supervised image retrieval models that support open-ended instructions.
- Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention134
A multi-layer hierarchical graph is exploited to build a hierarchical disentangled self-attention network, where each act is represented as a root-to-leaf route on the graph, and combinatorially many dialog act semantics can be modeled to control the neural response generation.
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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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