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20232025
most citedResolving Knowledge Conflicts in Large Language Models

6 citations · 23 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.CL2023★ 1 cited

On the Zero-Shot Generalization of Machine-Generated Text Detectors

Xiao Pu, Jingyu Zhang, Xiaochuang Han +2

The rampant proliferation of large language models, fluent enough to generate text indistinguishable from human-written language, gives unprecedented importance to the detection of…

cs.CL2023★ 1 cited

KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models

Yuyang Bai, Shangbin Feng, Vidhisha Balachandran +4

Large language models (LLMs) demonstrate remarkable performance on knowledge-intensive tasks, suggesting that real-world knowledge is encoded in their model parameters. However, be…

cs.CL2023★ 3 cited

SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation

Abe Bohan Hou, Jingyu Zhang, Tianxing He +7

Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design. To address this issue, we propose SemStamp, a robust sentence-level seman…

cs.CL2023★ 1 cited

Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models

Wenxuan Ding, Shangbin Feng, Yuhan Liu +4

We propose Knowledge Crosswords, a geometric knowledge reasoning benchmark consisting of incomplete knowledge networks bounded by structured factual constraints, where LLMs are tas…

cs.CL2023★ 6 cited

Resolving Knowledge Conflicts in Large Language Models

Yike Wang, Shangbin Feng, Heng Wang +4

Large language models (LLMs) often encounter knowledge conflicts, scenarios where discrepancy arises between the internal parametric knowledge of LLMs and non-parametric informatio…