29 citations · 32 across the 8 of their papers we have counts for
9 papers
Glasseek: A Transparent Recipe for Training and Trustworthy Evaluation of DeepSearch Agents
Cong Pang, Jiaqi Su, Chengrui Wu +13
Deep search enables language agents to solve complex information-seeking tasks through iterative web exploration. However, developing robust open-source agents remains challenging…
When History Is Multimodal: Rethinking Context Management for Long-Horizon Agents
Jiaqi Su, Cong Pang, Jiawei Hong +4
Long-horizon agents need a context manager to compress growing interaction histories into a bounded working context, via passive strategies or active strategies that decide how mem…
X+Slides: Benchmarking Audience-Conditioned Slide Generation
Haodong Chen, Xuanhe Zhou, Wei Zhou +6
Automatically generating slide decks from source documents is an important application of large language models (LLMs). Existing benchmarks primarily assess slide completeness and…
ICA: Information-Aware Credit Assignment for Visually Grounded Long-Horizon Information-Seeking Agents
Cong Pang, Xuyu Feng, Yujie Yi +8
Long-horizon reinforcement learning for information seeking agents remains difficult because terminal rewards reveal whether the final answer is correct, but not which acquired inf…
Not All Documents Are What You Need for Extracting Instruction Tuning Data
Chi Zhang, Huaping Zhong, Hongtao Li +11
Instruction tuning improves the performance of large language models (LLMs), but it heavily relies on high-quality training data. Recently, LLMs have been used to synthesize instru…
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…
WanJuan-CC: A Safe and High-Quality Open-sourced English Webtext Dataset
Jiantao Qiu, Haijun Lv, Zhenjiang Jin +23
This paper presents WanJuan-CC, a safe and high-quality open-sourced English webtext dataset derived from Common Crawl data. The study addresses the challenges of constructing larg…
InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
Huaiyuan Ying, Shuo Zhang, Linyang Li +19
The math abilities of large language models can represent their abstract reasoning ability. In this paper, we introduce and open-source our math reasoning LLMs InternLM-Math which…
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way
Kai Lv, Shuo Zhang, Tianle Gu +11
Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has…