1 citations · 1 across the 7 of their papers we have counts for
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UReason: Benchmarking Reasoning-to-Generation Alignment in Unified Multimodal Models
Cheng Yang, Chufan Shi, Bo Shui +7
Unified multimodal models (UMMs) aim to integrate multimodal understanding and generation within a unified architecture, yet it remains unclear to what extent textual and visual mo…
Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
Yuehan Qin, Shawn Li, Yi Nian +3
Large language models (LLMs) have shown substantial capacity for generating fluent, contextually appropriate responses. However, they can produce hallucinated outputs, especially w…
DecoPrompt : Decoding Prompts Reduces Hallucinations when Large Language Models Meet False Premises
Nan Xu, Xuezhe Ma
While large language models (LLMs) have demonstrated increasing power, they have also called upon studies on their hallucinated outputs that deviate from factually correct statemen…
LLM The Genius Paradox: A Linguistic and Math Expert's Struggle with Simple Word-based Counting Problems
Nan Xu, Xuezhe Ma
Interestingly, LLMs yet struggle with some basic tasks that humans find trivial to handle, e.g., counting the number of character r's in the word "strawberry". There are several po…
Evaluating Large Language Models on Controlled Generation Tasks
Jiao Sun, Yufei Tian, Wangchunshu Zhou +6
While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc,…