132 citations · 151 across the 12 of their papers we have counts for
14 papers
Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents
Jiajun Chen, Xinyue Chen, Yicheng Jiang +1
Personal-agent memory has largely focused on learning what users know, prefer, and want. However, knowing the user does not necessarily mean knowing what the user and the agent hav…
MIS-Bench: Benchmarking Multimodal LLMs for Psychotherapeutic Interpersonal Skills Assessment
Yuhan Lu, Yi Yao, Hua Shen +2
Multimodal large language models (MLLMs) are increasingly used as evaluators, yet their reliability in professional assessment tasks that require expert judgment remains unclear. W…
STAGE: Diagnosing Semantic Transfer at Grounded Execution in Embodied Agents
Baosheng Jin, Yushen Liang, Hua Shen
Embodied language grounding requires more than identifying the referent of an instruction: recovered semantics must also control the action an agent exposes. We study this missing…
Pseudo-Deliberation in Language Models: When Reasoning Fails to Align Values and Actions
Sushrita Rakshit, Hanwen Zhang, Hua Shen
Large language models (LLMs) are often evaluated based on their stated values, yet these do not reliably translate into their actions, a discrepancy termed "value-action gap." In t…
Value Alignment Tax: Measuring Value Trade-offs in LLM Alignment
Jiajun Chen, Hua Shen
Existing work on value alignment typically characterizes value relations statically, ignoring how alignment interventions, such as prompting, fine-tuning, or preference optimizatio…
ValueFlow: Measuring the Propagation of Value Perturbations in Multi-Agent LLM Systems
Jinnuo Liu, Chuke Liu, Hua Shen
Multi-agent large language model (LLM) systems increasingly consist of agents that observe and respond to one another's outputs. While value alignment is typically evaluated for is…
Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era
Xinyu Pi, Qisen Yang, Chuong Nguyen +1
LLMs are increasingly used to support qualitative research, yet existing systems produce outputs that vary widely--from trace-faithful summaries to theory-mediated explanations and…
Human-AI Interaction Alignment: Designing, Evaluating, and Evolving Value-Centered AI For Reciprocal Human-AI Futures
Hua Shen, Tiffany Knearem, Divy Thakkar +9
The rapid integration of generative AI into everyday life underscores the need to move beyond unidirectional alignment models that only adapt AI to human values. This workshop focu…
Beyond One-Way Influence: Bidirectional Opinion Dynamics in Multi-Turn Human-LLM Interactions
Yuyang Jiang, Longjie Guo, Yuchen Wu +3
Large language model (LLM)-powered chatbots are increasingly used for opinion exploration. Prior research examined how LLMs alter user views, yet little work extended beyond one-wa…
Epistemic Alignment: A Mediating Framework for User-LLM Knowledge Delivery
Nicholas Clark, Hua Shen, Bill Howe +1
LLMs increasingly serve as tools for knowledge acquisition, yet users cannot effectively specify how they want information presented. When users request that LLMs "cite reputable s…
Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values?
Hua Shen, Nicholas Clark, Tanushree Mitra
Existing research primarily evaluates the values of LLMs by examining their stated inclinations towards specific values. However, the "Value-Action Gap," a phenomenon rooted in env…
ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs
Hua Shen, Tiffany Knearem, Reshmi Ghosh +4
As AI systems become more advanced, ensuring their alignment with a diverse range of individuals and societal values becomes increasingly critical. But how can we capture fundament…
Position: Towards Bidirectional Human-AI Alignment
Hua Shen, Tiffany Knearem, Reshmi Ghosh +21
Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes…
A Design Space for Intelligent and Interactive Writing Assistants
Mina Lee, Katy Ilonka Gero, John Joon Young Chung +33
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to addr…