4 citations · 7 across the 6 of their papers we have counts for
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cs.AI2023★ 4 cited
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis
Li Du, Yequan Wang, Xingrun Xing +4
Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs. T…
cs.CL2023★ 2 cited
FLM-101B: An Open LLM and How to Train It with $100K Budget
Xiang Li, Yiqun Yao, Xin Jiang +10
Large language models (LLMs) are considered important approaches towards foundational machine intelligence, achieving remarkable success in Natural Language Processing and multimod…
cs.CL2023
nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales
Yiqun Yao, Siqi fan, Xiusheng Huang +10
As language models scale up, it becomes increasingly expensive to verify research ideas because conclusions on small models do not trivially transfer to large ones. A possible solu…