5 citations · 7 across the 8 of their papers we have counts for
8 papers
ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models
Hanxing Ding, Shuchang Tao, Liang Pang +5
Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools. Existing approaches fa…
On the Diminishing Returns of Complex Robust RAG Training in the Era of Powerful LLMs
Hanxing Ding, Shuchang Tao, Liang Pang +5
Retrieval-augmented generation (RAG) systems traditionally employ sophisticated training strategies to enhance robustness against retrieval noise. In this work, we investigate a cr…
Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models
Jingcheng Deng, Zihao Wei, Liang Pang +3
Recent knowledge editing methods have primarily focused on modifying structured knowledge in large language models. However, this task setting overlooks the fact that a significant…
When to Trust LLMs: Aligning Confidence with Response Quality
Shuchang Tao, Liuyi Yao, Hanxing Ding +6
Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlig…
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models
Zihao Wei, Jingcheng Deng, Liang Pang +3
The extensive utilization of large language models (LLMs) underscores the crucial necessity for precise and contemporary knowledge embedded within their intrinsic parameters. Exist…
Stable Knowledge Editing in Large Language Models
Zihao Wei, Liang Pang, Hanxing Ding +3
Efficient knowledge editing of large language models is crucial for replacing obsolete information or incorporating specialized knowledge on a large scale. However, previous method…
Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
Hanxing Ding, Liang Pang, Zihao Wei +2
Hallucinations present a significant challenge for large language models (LLMs). The utilization of parametric knowledge in generating factual content is constrained by the limited…
MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space
Hanxing Ding, Liang Pang, Zihao Wei +3
Multi-aspect controllable text generation aims to generate fluent sentences that possess multiple desired attributes simultaneously. Traditional methods either combine many operato…