1 citations · 3 across the 12 of their papers we have counts for
9 papers · 1 filter
ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting
Yuxing Tian, Fengran Mo, Weixu Zhang +2
The strong capabilities of recent Large Language Models (LLMs) have made them highly effective for zero-shot re-ranking task. Attention-based re-ranking methods, which derive relev…
OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG
Fengran Mo, Zhan Su, Yuchen Hui +6
The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The qual…
When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life
Xinyue Lou, Jinan Xu, Jingyi Yin +8
As Multimodal Large Language Models (MLLMs) become an indispensable assistant in human life, the unsafe content generated by MLLMs poses a danger to human behavior, perpetually ove…
Measuring the Impact of Lexical Training Data Coverage on Hallucination Detection in Large Language Models
Shuo Zhang, Fabrizio Gotti, Fengran Mo +1
Hallucination in large language models (LLMs) is a fundamental challenge, particularly in open-domain question answering. Prior work attempts to detect hallucination with model-int…
SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models
Rui Qi, Zhibo Man, Yufeng Chen +3
Recent developments have enabled Large Language Models (LLMs) to engage in complex reasoning tasks through deep thinking. However, the capacity of reasoning has not been successful…
Smooth Reading: Bridging the Gap of Recurrent LLM to Self-Attention LLM on Long-Context Tasks
Kai Liu, Zhan Su, Peijie Dong +4
Recently, recurrent large language models (Recurrent LLMs) with linear computational complexity have re-emerged as efficient alternatives to self-attention-based LLMs (Self-Attenti…