1.1k citations · 1.5k across the 38 of their papers we have counts for
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Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection
Zekun Li, Baolin Peng, Pengcheng He +1
Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, becoming increasingly crucial across various applications. However, this capability…
Augmenting Language Models with Long-Term Memory
Weizhi Wang, Li Dong, Hao Cheng +4
Existing large language models (LLMs) can only afford fix-sized inputs due to the input length limit, preventing them from utilizing rich long-context information from past inputs.…
Graph Reasoning for Question Answering with Triplet Retrieval
Shiyang Li, Yifan Gao, Haoming Jiang +5
Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which ar…
Bot or Human? Detecting ChatGPT Imposters with A Single Question
Hong Wang, Xuan Luo, Weizhi Wang +1
Large language models (LLMs) like GPT-4 have recently demonstrated impressive capabilities in natural language understanding and generation. However, there is a concern that they c…
Language Model Detoxification in Dialogue with Contextualized Stance Control
Jing Qian, Xifeng Yan
To reduce the toxic degeneration in a pretrained Language Model (LM), previous work on Language Model detoxification has focused on reducing the toxicity of the generation itself (…
Explanations from Large Language Models Make Small Reasoners Better
Shiyang Li, Jianshu Chen, Yelong Shen +9
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In thi…