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Found 22 papers, 10 papers with code
Table Fact Verification with Structure-Aware Transformer
Verifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning.
OIBench: Benchmarking Strong Reasoning Models with Olympiad in Informatics
As models become increasingly sophisticated, conventional algorithm benchmarks are increasingly saturated, underscoring the need for more challenging benchmarks to guide future improvements in algorithmic reasoning.
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
A total of 211 participants have registered in the structure track.
ViC-Bench: Benchmarking Visual-Interleaved Chain-of-Thought Capability in MLLMs with Free-Style Intermediate State Representations
Visual-Interleaved Chain-of-Thought (VI-CoT) enables MLLMs to continually update their understanding and decisions based on step-wise intermediate visual states (IVS), much like a human would, which demonstrates impressive success in various tasks, thereby leading to emerged advancements in related benchmarks.
Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement
Large Language Models (LLMs) have shown impressive capabilities across various tasks but remain vulnerable to meticulously crafted jailbreak attacks.
Audio Turing Test: Benchmarking the Human-likeness of Large Language Model-based Text-to-Speech Systems in Chinese
Recent advances in large language models (LLMs) have significantly improved text-to-speech (TTS) systems, enhancing control over speech style, naturalness, and emotional expression, which brings TTS Systems closer to human-level performance.
Ask, Fail, Repeat: Meeseeks, an Iterative Feedback Benchmark for LLMs' Multi-turn Instruction-Following Ability
The ability to follow instructions accurately is fundamental for Large Language Models (LLMs) to serve as reliable agents in real-world applications.
TokenFocus-VQA: Enhancing Text-to-Image Alignment with Position-Aware Focus and Multi-Perspective Aggregations on LVLMs
While text-to-image (T2I) generation models have achieved remarkable progress in recent years, existing evaluation methodologies for vision-language alignment still struggle with the fine-grained semantic matching.
Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content
While significant progress has been made in developing objective models to assess these dimensions, the performance of such models heavily relies on the scale and quality of human annotations.
Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration
We propose DPT-Agent, a novel language agent framework that integrates System 1 and System 2 for efficient real-time simultaneous human-AI collaboration.
Who's the MVP? A Game-Theoretic Evaluation Benchmark for Modular Attribution in LLM Agents
Large Language Model (LLM) agents frameworks often employ modular architectures, incorporating components such as planning, reasoning, action execution, and reflection to tackle complex tasks.
Length Desensitization in Direct Preference Optimization
Direct Preference Optimization (DPO) is widely utilized in the Reinforcement Learning from Human Feedback (RLHF) phase to align Large Language Models (LLMs) with human preferences, thereby enhancing both their harmlessness and efficacy.
Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs
Extensive experiments on 12 mainstream MLLMs, such as GPT-4V and Gemini-Pro Vision, demonstrate that these models exhibit significant hallucinations on Hallu-PI, which is not observed in unperturbed scenarios.
Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis
To facilitate research in this new task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE, and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily
Finally, we analyze the failure of LLMs defense from the perspective of prompt execution priority, and propose corresponding defense strategies.
Exchanging-based Multimodal Fusion with Transformer
To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer.
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type.
Adap-$τ$: Adaptively Modulating Embedding Magnitude for Recommendation
Recent years have witnessed the great successes of embedding-based methods in recommender systems.
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion
Specifically, they suffer from two main limitations: 1) existing Graph Convolutional Network (GCN) methods in hyperbolic space rely on tangent space approximation, which would incur approximation error in representation learning, and 2) due to the lack of inner product operation definition in hyperbolic space, existing methods can only measure the plausibility of facts (links) with hyperbolic distance, which is difficult to capture complex data patterns.
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
To sufficiently exploit such important information for recommendation, it is essential to disentangle the benign popularity bias caused by item quality from the harmful popularity bias caused by conformity.
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks.
Revealing Multiple Layers of Hidden Community Structure in Networks
Additionally, on both real and synthetic networks containing a hidden ground-truth community structure, HICODE uncovers this structure better than any baseline algorithms that we compared against.
Social and Information Networks Physics and Society
