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Xunzhi Wang

7 accepted papers

2025

ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area

AAAI 2025technical

Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully handled by existing chemical LLMs. This brings a growing…

2025

Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning

CVPR 2025poster

Vision-language models (VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues like hallucinated image understandings or unrefined reasoning paths. To address these challenges, we introduce Critic-V,…

2025

DEQA: Descriptions Enhanced Question-Answering Framework for Multimodal Aspect-Based Sentiment Analysis

AAAI 2025technical

Multimodal aspect-based sentiment analysis (MABSA) integrates text and images to perform fine-grained sentiment analysis on specific aspects, enhancing the understanding of user opinions in various applications. Existing methods use modality alignment for information interaction and fusion between i…

2025

SocialEval: Evaluating Social Intelligence of Large Language Models

ACL 2025long

LLMs exhibit promising Social Intelligence (SI) in modeling human behavior, raising the need to evaluate LLMs’ SI and their discrepancy with humans. SI equips humans with interpersonal abilities to behave wisely in navigating social interactions to achieve social goals. This presents an operational…

2025

UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions

ACL 2025finding

Despite recent progress in systematic evaluation frameworks, benchmarking the uncertainty of large language models (LLMs) remains a highly challenging task. Existing methods for benchmarking the uncertainty of LLMs face three key challenges: the need for internal model access, additional training, o…

2024

Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs

ACL 2024findings

Controlled Text Generation (CTG) aims to produce texts that exhibit specific desired attributes. In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG). This framework utilizes an attribute scorer…

2024

Is Compound Aspect-Based Sentiment Analysis Addressed by LLMs?

EMNLP 2024finding

Aspect-based sentiment analysis (ABSA) aims to predict aspect-based elements from the given text, mainly including four elements, i.e., aspect category, sentiment polarity, aspect term, and opinion term. Extracting pair, triple, or quad of elements is defined as compound ABSA. Due to its challenges…

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