47 citations · 74 across the 15 of their papers we have counts for
15 papers
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective
Yiyao Yu, Yuxiang Zhang, Dongdong Zhang +9
Large Language Models (LLMs) have made notable progress in mathematical reasoning, yet often rely on single-paradigm reasoning, limiting their effectiveness across diverse tasks. W…
Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models
Yuxiang Zhang, Xin Fan, Junjie Wang +4
Recent advancements in large language models (LLMs) integrated with external tools and APIs have successfully addressed complex tasks by using in-context learning or fine-tuning. D…
HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing
Jing Chen, Xinyu Zhu, Cheng Yang +8
Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in natural language processing. In particular, la…
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models
Yuxiang Zhang, Jing Chen, Junjie Wang +10
Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. Due to the lack of benchmarks, the community has yet to fully understand the…
PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents
Junjie Wang, Yuxiang Zhang, Minghao Liu +19
Recent advancements in large multimodal models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks. However, persistent ch…
ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation
Cheng Yang, Chufan Shi, Yaxin Liu +11
We introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes information-i…
EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval
Yiyao Yu, Junjie Wang, Yuxiang Zhang +3
Artificial intelligence (AI) technologies should adhere to human norms to better serve our society and avoid disseminating harmful or misleading information, particularly in Conver…
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective
Ping Yang, Junyu Lu, Ruyi Gan +4
We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition…
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension
Yuxiang Zhang, Junjie Wang, Xinyu Zhu +2
Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research…
Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks
Tetsuya Sakai, Sijie Tao, Maria Maistro +7
Unfortunately, the official English (sub)task results reported in the NTCIR-14 WWW-2, NTCIR-15 WWW-3, and NTCIR-16 WWW-4 overview papers are incorrect due to noise in the official…
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective
Ping Yang, Junjie Wang, Ruyi Gan +6
We propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text classifica…
Solving Math Word Problems via Cooperative Reasoning induced Language Models
Xinyu Zhu, Junjie Wang, Lin Zhang +4
Large-scale pre-trained language models (PLMs) bring new opportunities to challenging problems, especially those that need high-level intelligence, such as the math word problem (M…
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
Yatai Ji, Junjie Wang, Yuan Gong +6
Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our i…
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
Jiaxing Zhang, Ruyi Gan, Junjie Wang +22
Nowadays, foundation models become one of fundamental infrastructures in artificial intelligence, paving ways to the general intelligence. However, the reality presents two urgent…
Towards No.1 in CLUE Semantic Matching Challenge: Pre-trained Language Model Erlangshen with Propensity-Corrected Loss
Junjie Wang, Yuxiang Zhang, Ping Yang +1
This report describes a pre-trained language model Erlangshen with propensity-corrected loss, the No.1 in CLUE Semantic Matching Challenge. In the pre-training stage, we construct…