256 citations · 278 across the 8 of their papers we have counts for
9 papers · 1 filter
NLL-Guided Full-Attention Layer Selection for Training-Free Sliding-Window Adaptation
Qiong Tang, Xiangkun Hu, Xiangyang Liu +2
Hybrid attention models that mix full and sliding-window attention across layers offer a promising approach to efficient long-context inference, but the critical question of \emph{…
Making Large Language Models Better Reasoners with Orchestrated Streaming Experiences
Xiangyang Liu, Junliang He, Xipeng Qiu
Large language models (LLMs) can perform complex reasoning by generating intermediate thoughts under zero-shot or few-shot settings. However, zero-shot prompting always encounters…
DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels
Zhe Xu, Jiasheng Ye, Xiaoran Liu +8
Recently, significant efforts have been devoted to enhancing the long-context capabilities of Large Language Models (LLMs), particularly in long-context reasoning. To facilitate th…
Can AI Assistants Know What They Don't Know?
Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu +7
Recently, AI assistants based on large language models (LLMs) show surprising performance in many tasks, such as dialogue, solving math problems, writing code, and using tools. Alt…
Flames: Benchmarking Value Alignment of LLMs in Chinese
Kexin Huang, Xiangyang Liu, Qianyu Guo +9
The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, howeve…
Evaluating Hallucinations in Chinese Large Language Models
Qinyuan Cheng, Tianxiang Sun, Wenwei Zhang +8
In this paper, we establish a benchmark named HalluQA (Chinese Hallucination Question-Answering) to measure the hallucination phenomenon in Chinese large language models. HalluQA c…