activity
20162025
most citedToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

72 citations · 136 across the 18 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.CL2024★ 1 cited

KBAlign: Efficient Self Adaptation on Specific Knowledge Bases

Zheni Zeng, Yuxuan Chen, Shi Yu +7

Although retrieval-augmented generation (RAG) remains essential for knowledge-based question answering (KBQA), current paradigms face critical challenges under specific domains. Ex…

cs.CL2024★ 1 cited

LLMMapReduce: Simplified Long-Sequence Processing using Large Language Models

Zihan Zhou, Chong Li, Xinyi Chen +11

Enlarging the context window of large language models (LLMs) has become a crucial research area, particularly for applications involving extremely long texts. In this work, we prop…

cs.CL2024

The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning

Bingxiang He, Ning Ding, Cheng Qian +10

Understanding alignment techniques begins with comprehending zero-shot generalization brought by instruction tuning, but little of the mechanism has been understood. Existing work…

cs.AI2024★ 7 cited

Scaling Large Language Model-based Multi-Agent Collaboration

Chen Qian, Zihao Xie, YiFei Wang +9

Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning. Ins…

cs.DC2024★ 1 cited

Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training

Ao Sun, Weilin Zhao, Xu Han +5

The emergence of large language models (LLMs) relies heavily on distributed training strategies, among which pipeline parallelism plays a crucial role. As LLMs' training sequence l…

cs.CL2024★ 3 cited

RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness

Tianyu Yu, Haoye Zhang, Qiming Li +13

Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models. This leaves the community without foundational…

cs.CL2024

Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models

Ning Ding, Yulin Chen, Ganqu Cui +6

Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that stri…

cs.CL2024

UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset

Haoyu Wang, Shuo Wang, Yukun Yan +9

Open-source large language models (LLMs) have gained significant strength across diverse fields. Nevertheless, the majority of studies primarily concentrate on English, with only l…