51 citations · 368 across the 64 of their papers we have counts for
14 papers · 1 filter
OpenCompass: A Universal Evaluation Platform for Large Language Models
Maosong Cao, Kai Chen, Haodong Duan +27
In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs). With the…
WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation
Shuangrui Ding, Xuanlang Dai, Long Xing +14
Large language and vision-language models increasingly power agents that act on a user's behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still…
A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Ming Hu, Chenglong Ma, Wei Li +117
Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the compl…
GDI-Bench: A Benchmark for General Document Intelligence with Vision and Reasoning Decoupling
Siqi Li, Yufan Shen, Xiangnan Chen +13
The rapid advancement of multimodal large language models (MLLMs) has profoundly impacted the document domain, creating a wide array of application scenarios. This progress highlig…
BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning
Beichen Zhang, Yuhong Liu, Xiaoyi Dong +6
Large language models (LLMs) have demonstrated impressive ability in solving complex mathematical problems with multi-step reasoning and can be further enhanced with well-designed…
Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement
Maosong Cao, Taolin Zhang, Mo Li +5
The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of Large Language Models (LLMs). However, as LLMs become more ad…
Redundancy Principles for MLLMs Benchmarks
Zicheng Zhang, Xiangyu Zhao, Xinyu Fang +6
With the rapid iteration of Multi-modality Large Language Models (MLLMs) and the evolving demands of the field, the number of benchmarks produced annually has surged into the hundr…
CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution
Maosong Cao, Alexander Lam, Haodong Duan +3
Efficient and accurate evaluation is crucial for the continuous improvement of large language models (LLMs). Among various assessment methods, subjective evaluation has garnered si…
ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs
Jingming Zhuo, Songyang Zhang, Xinyu Fang +3
Large language models (LLMs) have demonstrated impressive capabilities across various tasks, but their performance is highly sensitive to the prompts utilized. This variability pos…
NeedleBench: Evaluating LLM Retrieval and Reasoning Across Varying Information Densities
Mo Li, Songyang Zhang, Taolin Zhang +3
The capability of large language models to handle long-context information is crucial across various real-world applications. Existing evaluation methods often rely either on real-…
MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark
Hongwei Liu, Zilong Zheng, Yuxuan Qiao +7
Recent advancements in large language models (LLMs) have showcased significant improvements in mathematics. However, traditional math benchmarks like GSM8k offer a unidimensional p…
Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks
Chonghua Wang, Haodong Duan, Songyang Zhang +2
Recently, the large language model (LLM) community has shown increasing interest in enhancing LLMs' capability to handle extremely long documents. As various long-text techniques a…
InternLM2 Technical Report
Zheng Cai, Maosong Cao, Haojiong Chen +97
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advan…
BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues
Haodong Duan, Jueqi Wei, Chonghua Wang +5
Interacting with human via high-quality multi-turn dialogues is a key feature of large language models (LLMs). However, human-based evaluation of such capability involves intensive…