2.1k citations · 2.1k across the 20 of their papers we have counts for
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Can Large Language Models Simulate Human Cognition Beyond Behavioral Imitation?
Yuxuan Gu, Lunjun Liu, Xiaocheng Feng +4
An essential problem in artificial intelligence is whether LLMs can simulate human cognition or merely imitate surface-level behaviors, while existing datasets suffer from either s…
Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization
Lei Huang, Xiaocheng Feng, Weitao Ma +9
Ensuring contextual faithfulness in retrieval-augmented large language models (LLMs) is crucial for building trustworthy information-seeking systems, particularly in long-form ques…
Length Controlled Generation for Black-box LLMs
Yuxuan Gu, Wenjie Wang, Xiaocheng Feng +5
Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is…
Advancing Large Language Model Attribution through Self-Improving
Lei Huang, Xiaocheng Feng, Weitao Ma +7
Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems. Howe…
Extending Context Window of Large Language Models from a Distributional Perspective
Yingsheng Wu, Yuxuan Gu, Xiaocheng Feng +5
Scaling the rotary position embedding (RoPE) has become a common method for extending the context window of RoPE-based large language models (LLMs). However, existing scaling metho…
Learning Fine-Grained Grounded Citations for Attributed Large Language Models
Lei Huang, Xiaocheng Feng, Weitao Ma +8
Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with…
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis
Weitao Ma, Xiaocheng Feng, Weihong Zhong +4
Large language model unlearning has garnered increasing attention due to its potential to address security and privacy concerns, leading to extensive research in the field. However…
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
Liang Zhao, Xiachong Feng, Xiaocheng Feng +6
Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities. Nevertheless, all Transformer-based models including L…
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
Lei Huang, Weijiang Yu, Weitao Ma +8
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition. Never…
Controllable Text Generation via Probability Density Estimation in the Latent Space
Yuxuan Gu, Xiaocheng Feng, Sicheng Ma +4
Previous work on controllable text generation has explored the idea of control from the latent space, such as optimizing a representation with attribute-related classifiers or samp…