5 papers
Can Large Multimodal Models Actively Recognize Faulty Inputs? A Systematic Evaluation Framework of Their Input Scrutiny Ability
Haiqi Yang, Jinzhe Li, Gengxu Li +2
Large Multimodal Models (LMMs) have witnessed remarkable growth, showcasing formidable capabilities in handling intricate multimodal tasks with exceptional performance. Recent rese…
Refining Critical Thinking in LLM Code Generation: A Faulty Premise-based Evaluation Framework
Jialin Li, Jinzhe Li, Gengxu Li +2
With the advancement of code generation capabilities in large language models (LLMs), their reliance on input premises has intensified. When users provide inputs containing faulty…
Don't Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models
Jinzhe Li, Gengxu Li, Yi Chang +1
Large language models (LLMs) have witnessed rapid advancements, demonstrating remarkable capabilities. However, a notable vulnerability persists: LLMs often uncritically accept fla…
JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning
Zhaolu Kang, Hongtian Cai, Xiangyang Ji +2
In recent years, Unsupervised Domain Adaptation (UDA) has gained significant attention in the field of Natural Language Processing (NLP) owing to its ability to enhance model gener…
StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following
Jinnan Li, Jinzhe Li, Yue Wang +2
Multi-turn instruction following capability constitutes a core competency of large language models (LLMs) in real-world applications. Existing evaluation benchmarks predominantly f…