activity
20222025
most citedRepresenting Affect Information in Word Embeddings

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2025

AI Deception: Risks, Dynamics, and Controls

Boyuan Chen, Sitong Fang, Jiaming Ji +56

As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an…

cs.AI2025

Mitigating Deceptive Alignment via Self-Monitoring

Jiaming Ji, Wenqi Chen, Kaile Wang +8

Modern large language models rely on chain-of-thought (CoT) reasoning to achieve impressive performance, yet the same mechanism can amplify deceptive alignment, situations in which…

cs.AI2025

Generative RLHF-V: Learning Principles from Multi-modal Human Preference

Jiayi Zhou, Jiaming Ji, Boyuan Chen +6

Training multi-modal large language models (MLLMs) that align with human intentions is a long-term challenge. Traditional score-only reward models for alignment suffer from low acc…

cs.AI2025

InterMT: Multi-Turn Interleaved Preference Alignment with Human Feedback

Boyuan Chen, Donghai Hong, Jiaming Ji +12

As multimodal large models (MLLMs) continue to advance across challenging tasks, a key question emerges: What essential capabilities are still missing? A critical aspect of human l…

cs.AI2024

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Jiaming Ji, Jiayi Zhou, Hantao Lou +16

Reinforcement learning from human feedback (RLHF) has proven effective in enhancing the instruction-following capabilities of large language models; however, it remains underexplor…

cs.CL2022★ 1 cited

Representing Affect Information in Word Embeddings

Yuhan Zhang, Wenqi Chen, Ruihan Zhang +1

A growing body of research in natural language processing (NLP) and natural language understanding (NLU) is investigating human-like knowledge learned or encoded in the word embedd…