9 accepted papers
Conditional image generation has gained significant attention for its ability to personalize content. However, the field faces challenges in developing task-agnostic, reliable, and explainable evaluation metrics. This paper introduces CIGEval, a unified agentic framework for comprehensive evaluation…
Language agents have demonstrated remarkable emergent social behaviors within simulated sandbox environments. However, the characterization of these agents has been constrained by static prompts that outline their profiles, highlighting a gap in achieving simulations that closely mimic real-life int…
Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base. Existing MEL methods mainly focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters, which can be prohibitively c…
We propose MultiSkill, an evaluation protocol that assesses large multimodal models (LMMs) across multiple fine-grained skills for alignment with human values. Recent LMMs have shown various intriguing abilities, such as solving graph theory problems and explaining visual jokes. However, existing mu…
Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability. Previous methods largely focus on the candidate retrieval stage and ignore the essential candidate ranking stage, which disambiguates among entities and makes the final linking…
In the field of Large Language Models (LLMs), researchers are increasingly exploring their effectiveness across a wide range of tasks. However, a critical area that requires further investigation is the interpretability of these models, particularly the ability to generate rational explanations for…
Biomedical entity linking (EL) is the task of linking mentions in a biomedical document to corresponding entities in a knowledge base (KB). The challenge in biomedical EL lies in leveraging mention context to select the most appropriate entity among possible candidates. Although some EL models achie…
Event Causality Identification (ECI) aims to identify the causality between a pair of event mentions in a document, which is composed of sentence-level ECI (SECI) and document-level ECI (DECI). Previous work applies various reasoning models to identify the implicit event causality. However, they ind…
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base. One of the key challenges comes from insufficient labeled data for specific domains. Although dense retrievers have achieved excellent performance on several benchmarks, their performance decreases s…