activity
20232025
most citedResponsible AI Considerations in Text Summarization Research: A Review of Current Practices

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2025

Stochastic Chameleons: Irrelevant Context Hallucinations Reveal Class-Based (Mis)Generalization in LLMs

Ziling Cheng, Meng Cao, Marc-Antoine Rondeau +1

The widespread success of large language models (LLMs) on NLP benchmarks has been accompanied by concerns that LLMs function primarily as stochastic parrots that reproduce texts si…

cs.CL2025

Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation

Ziling Cheng, Meng Cao, Leila Pishdad +2

Final-answer-based metrics are commonly used for evaluating large language models (LLMs) on math word problems, often taken as proxies for reasoning ability. However, such metrics…

cs.CL2024

Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations

Lei Yu, Meng Cao, Jackie Chi Kit Cheung +1

State-of-the-art language models (LMs) sometimes generate non-factual hallucinations that misalign with world knowledge. To explore the mechanistic causes of these hallucinations,…

cs.CL20231 cited

Responsible AI Considerations in Text Summarization Research: A Review of Current Practices

Yu Lu Liu, Meng Cao, Su Lin Blodgett +3

AI and NLP publication venues have increasingly encouraged researchers to reflect on possible ethical considerations, adverse impacts, and other responsible AI issues their work mi…

cs.CL2023

Successor Features for Efficient Multisubject Controlled Text Generation

Meng Cao, Mehdi Fatemi, Jackie Chi Kit Cheung +1

While large language models (LLMs) have achieved impressive performance in generating fluent and realistic text, controlling the generated text so that it exhibits properties such…