26 citations · 42 across the 8 of their papers we have counts for
7 papers · 1 filter
Spine-Branch Coordination for Multi-agent Computer Use
Mian Zhang, Manasi Sharma, Sheng Zhang +5
Computer use agents (CUAs) are increasingly deployed as multi-agent systems that decompose a task into multiple subtasks executed across parallel virtual machines (VMs). However, a…
Sycophancy Towards Researchers Drives Performative Misalignment
David D. Baek, Xinnuo Li, Anay Gupta +4
The increasing situational awareness of language models raises safety concerns: models might be aware when they are evaluated, and adjust their behavior to evade monitoring and res…
ReIFE: Re-evaluating Instruction-Following Evaluation
Yixin Liu, Kejian Shi, Alexander R. Fabbri +5
The automatic evaluation of instruction following typically involves using large language models (LLMs) to assess response quality. However, there is a lack of comprehensive evalua…
Large Language Models as Misleading Assistants in Conversation
Betty Li Hou, Kejian Shi, Jason Phang +3
Large Language Models (LLMs) are able to provide assistance on a wide range of information-seeking tasks. However, model outputs may be misleading, whether unintentionally or in ca…
ODSum: New Benchmarks for Open Domain Multi-Document Summarization
Yijie Zhou, Kejian Shi, Wencai Zhang +3
Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries. With a more inter-related document set…
Pretraining Language Models with Human Preferences
Tomasz Korbak, Kejian Shi, Angelica Chen +5
Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, persona…
Automatic Error Analysis for Document-level Information Extraction
Aliva Das, Xinya Du, Barry Wang +4
Document-level information extraction (IE) tasks have recently begun to be revisited in earnest using the end-to-end neural network techniques that have been successful on their se…