2 citations · 5 across the 8 of their papers we have counts for
8 papers
Investigating and Alleviating Harm Amplification in LLM Interactions
Ruohao Guo, Wei Xu, Alan Ritter
Large language models (LLMs) can serve as helpful assistants, yet they can equally function as harm amplifiers that enable malicious users to achieve harmful outcomes beyond their…
Tree-based Dialogue Reinforced Policy Optimization for Red-Teaming Attacks
Ruohao Guo, Afshin Oroojlooy, Roshan Sridhar +3
Despite recent rapid progress in AI safety, current large language models remain vulnerable to adversarial attacks in multi-turn interaction settings, where attackers strategically…
Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers
Xingyue Huang, Rishabh, Gregor Franke +43
Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RL…
How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation
Ruohao Guo, Wei Xu, Alan Ritter
As Large Language Models (LLMs) are widely deployed in diverse scenarios, the extent to which they could tacitly spread misinformation emerges as a critical safety concern. Current…
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
Jiangshu Du, Yibo Wang, Wenting Zhao +37
This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and ques…
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations
Deqing Fu, Ruohao Guo, Ghazal Khalighinejad +5
Current foundation models exhibit impressive capabilities when prompted either with text only or with both image and text inputs. But do their capabilities change depending on the…
Improved Instruction Ordering in Recipe-Grounded Conversation
Duong Minh Le, Ruohao Guo, Wei Xu +1
In this paper, we study the task of instructional dialogue and focus on the cooking domain. Analyzing the generated output of the GPT-J model, we reveal that the primary challenge…
Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding
Ruohao Guo, Wei Xu, Alan Ritter
Language style is often used by writers to convey their intentions, identities, and mastery of language. In this paper, we show that current large language models struggle to captu…