6 citations · 15 across the 8 of their papers we have counts for
Showing 2024 · cs.CLShow all
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cs.CL2024★ 6 cited
Paraphrase Types Elicit Prompt Engineering Capabilities
Jan Philip Wahle, Terry Ruas, Yang Xu +1
Much of the success of modern language models depends on finding a suitable prompt to instruct the model. Until now, it has been largely unknown how variations in the linguistic ex…
cs.CL2024★ 3 cited
RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models
Xiangkun Hu, Dongyu Ru, Lin Qiu +7
Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-tri…
cs.CL2024★ 4 cited
MULTI: Multimodal Understanding Leaderboard with Text and Images
Zichen Zhu, Yang Xu, Lu Chen +11
The rapid development of multimodal large language models (MLLMs) raises the question of how they compare to human performance. While existing datasets often feature synthetic or o…