5 citations · 18 across the 17 of their papers we have counts for
17 papers
ACADREASON: Exploring the Limits of Reasoning Models with Academic Research Problems
Xin Gui, King Zhu, JinCheng Ren +17
In recent years, the research focus of large language models (LLMs) and agents has shifted increasingly from demonstrating novel capabilities to complex reasoning and tackling chal…
CryptoX : Compositional Reasoning Evaluation of Large Language Models
Jiajun Shi, Chaoren Wei, Liqun Yang +7
The compositional reasoning capacity has long been regarded as critical to the generalization and intelligence emergence of large language models LLMs. However, despite numerous re…
A Comprehensive Survey on Long Context Language Modeling
Jiaheng Liu, Dawei Zhu, Zhiqi Bai +34
Efficient processing of long contexts has been a persistent pursuit in Natural Language Processing. With the growing number of long documents, dialogues, and other textual data, it…
OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Training
Yijiong Yu, Ziyun Dai, Zekun Wang +3
Large language models (LLMs) have demonstrated remarkable capabilities, but their success heavily relies on the quality of pretraining corpora. For Chinese LLMs, the scarcity of hi…
Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey
Liang Chen, Zekun Wang, Shuhuai Ren +24
Building on the foundations of language modeling in natural language processing, Next Token Prediction (NTP) has evolved into a versatile training objective for machine learning ta…
M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation
Jiaheng Liu, Ken Deng, Congnan Liu +13
Repository-level code completion has drawn great attention in software engineering, and several benchmark datasets have been introduced. However, existing repository-level code com…
PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment
Zekun Moore Wang, Shawn Wang, Kang Zhu +5
Alignment of large language models (LLMs) involves training models on preference-contrastive output pairs to adjust their responses according to human preferences. To obtain such c…
MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models
Pei Wang, Yanan Wu, Zekun Wang +12
Large Language Models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users. Recently, many tool-use benc…
HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models
Haoran Que, Feiyu Duan, Liqun He +11
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks (e.g., long-context understanding), and many benchmarks have been proposed.…
MIO: A Foundation Model on Multimodal Tokens
Zekun Wang, King Zhu, Chunpu Xu +14
In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, aut…
McEval: Massively Multilingual Code Evaluation
Linzheng Chai, Shukai Liu, Jian Yang +15
Code large language models (LLMs) have shown remarkable advances in code understanding, completion, and generation tasks. Programming benchmarks, comprised of a selection of code c…
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…
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
Ziqiang Liu, Feiteng Fang, Xi Feng +23
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks. In response, numerous challe…
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Yuelin Bai, Xinrun Du, Yiming Liang +19
Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs). However, there remains a noticeable gap in instructi…
PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents
Qisen Yang, Zekun Wang, Honghui Chen +6
Psychological measurement is essential for mental health, self-understanding, and personal development. Traditional methods, such as self-report scales and psychologist interviews,…
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
Ge Zhang, Xinrun Du, Bei Chen +19
As the capabilities of large multimodal models (LMMs) continue to advance, evaluating the performance of LMMs emerges as an increasing need. Additionally, there is an even larger g…
Align on the Fly: Adapting Chatbot Behavior to Established Norms
Chunpu Xu, Steffi Chern, Ethan Chern +6
In this paper, we aim to align large language models with the ever-changing, complex, and diverse human values (e.g., social norms) across time and locations. This presents a chall…