3 citations · 4 across the 8 of their papers we have counts for
9 papers
OpenThoughts-Agent: Data Recipes for Agentic Models
Negin Raoof, Richard Zhuang, Marianna Nezhurina +47
Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts…
QMFOL: Benchmarking Large Language Model Reasoning via Quantifiable Monadic First-Order Logic Test Case Generation
Xinyi Zheng, Ling Shi, Tianlong Yu +3
Large Language Models (LLMs) have made significant progress in reasoning, particularly in deductive reasoning, which is crucial for high-stakes decision-making. As models improve,…
From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models
Ling Shi, Xinwei Wu, Xiaohu Zhao +7
While mechanistic interpretability tools like Sparse Autoencoders (SAEs) can uncover meaningful features within Large Language Models (LLMs), a critical gap remains in transforming…
DEP: A Decentralized Large Language Model Evaluation Protocol
Jianxiang Peng, Junhao Li, Hongxiang Wang +15
With the rapid development of Large Language Models (LLMs), a large number of benchmarks have been proposed. However, most benchmarks lack unified evaluation standard and require t…
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan
Lei Yang, Leiyu Pan, Bojian Xiong +14
Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, yet their performance remains heavily biased toward high-res…
ProBench: Benchmarking Large Language Models in Competitive Programming
Lei Yang, Renren Jin, Ling Shi +3
With reasoning language models such as OpenAI-o3 and DeepSeek-R1 emerging, large language models (LLMs) have entered a new phase of development. However, existing benchmarks for co…
Large Language Model Safety: A Holistic Survey
Dan Shi, Tianhao Shen, Yufei Huang +10
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural lang…
CRiskEval: A Chinese Multi-Level Risk Evaluation Benchmark Dataset for Large Language Models
Ling Shi, Deyi Xiong
Large language models (LLMs) are possessed of numerous beneficial capabilities, yet their potential inclination harbors unpredictable risks that may materialize in the future. We h…
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
Chuang Liu, Linhao Yu, Jiaxuan Li +11
The rapid development of Chinese large language models (LLMs) poses big challenges for efficient LLM evaluation. While current initiatives have introduced new benchmarks or evaluat…