62 citations · 251 across the 26 of their papers we have counts for
8 papers · 1 filter
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach
Yanchen Liu, Mingyu Derek Ma, Wenna Qin +5
Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals' mental processes that is not observable. Existing susceptibi…
Unlearn What You Want to Forget: Efficient Unlearning for LLMs
Jiaao Chen, Diyi Yang
Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data, however, this process might suffer from privacy is…
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
Kaijie Zhu, Jiaao Chen, Jindong Wang +3
Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns are raised about potential data contamination in their conside…
Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models
Jiaao Chen, Xiaoman Pan, Dian Yu +4
We investigate how to elicit compositional generalization capabilities in large language models (LLMs). Compositional generalization empowers LLMs to solve complex problems by comb…
Can Large Language Models Transform Computational Social Science?
Caleb Ziems, William Held, Omar Shaikh +3
Large Language Models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify a…
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise
Jiaao Chen, Aston Zhang, Mu Li +2
Diffusion models that are based on iterative denoising have been recently proposed and leveraged in various generation tasks like image generation. Whereas, as a way inherently bui…
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang +3
Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i.e., with…
Parameter-Efficient Fine-Tuning Design Spaces
Jiaao Chen, Aston Zhang, Xingjian Shi +3
Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning, using fewer trainable parameters. Several strategies (e.g., Adapters, prefix tuning, BitFit,…