Towards Helpful, Honest, and Harmless Information Communication with Human-Model Alignment and Multi-Cultural Understanding

Speaker: Yi Ren FUNG
         Department of Computer Science
         University of Illinois Urbana-Champaign

Title:  "Towards Helpful, Honest, and Harmless Information Communication
         with Human-Model Alignment and Multi-Cultural Understanding"

Date:    Monday; 19 February 2024

Time:    4:00pm - 5:00pm

Venue:   Lecture Theater F
         (Leung Yat Sing Lecture Theater), near lift 25/26


In recent years, language and multimedia models have made significant 
advancements, achieving remarkable performance on a large variety of 
tasks, including question answering, summarization, scientific reasoning, 
procedural understanding, and action planning, along with demonstrating 
robust zero-shot/few-shot capabilities, bolstered by model scaling and 
innovative training techniques. Despite the exciting progress, ensuring 
these models align with fundamental constitutional principles remains a 
critical challenge. In this talk, we present a roadmap for foundation 
models as digital agents that are not only technologically advanced and 
functionally robust, but also sociocultural-aware and ethically grounded. 
We begin by delving into advanced mechanisms (i.e., the InfoSurgeon 
architecture) for identifying information inconsistencies in textual or 
multimedia content, along with novel strategies to counter the undesirable 
phenomena of generative model hallucination. Moreover, we introduce norm 
discovery with self-verification on-the-fly (i.e., the NormSAGE 
framework)as a promising solution for the explainable detection of 
real-world norm violation occurrences and for guiding harmless language 
model response generations. Particularly, we emphasize the importance of 
our investigative efforts in massively multicultural knowledge acquisition 
as a vital component to enrich model understanding of norms across diverse 
societal groups, ensuring more accurate and respectful human-centered 
interactions. By addressing these research problems and opportunities, we 
can help reinforce the relevance and responsibility of these foundational 
language and multimedia AI models in promoting healthy information 
communication in our increasingly interconnected world.


Yi R. Fung is a final year PhD student in computer science at the 
University of Illinois Urbana-Champaign, advised by Heng Ji. Her research 
specialization lies in AI/NLP and computational social science, with a 
particular focus on addressing the fundamental research questions of 
human-model alignment and 'helpful/honest/harmless' healthy information 
communication. Yi is one of the first researchers who proposed 
fine-grained knowledge-element level misinformation detection in 
multimedia news documents, along with a novel approach of event/entity 
manipulation for constructing targeted dataset that serves as a benchmark 
for this important task. In addition, she is the first to formally 
introduce norm discovery from conversation on-the-fly, and largely 
extended the scope of multicultural social norm knowledge acquisition for 
language model human-centered awareness and norm violation detection. Yi's 
research not only boldly addresses crucial emerging interdisciplinary 
problem domains, but also pioneers advancements in core NLP reasoning 
techniques, including multimedia knowledge-guided reasoning and language 
model prompting-based knowledge elicitation with self-verification 
mechanisms. Moreover, she has also been a leading student driver of 
several multi-million dollar national-level grant projects, such as 
SemaFor and CCU, achieving top scores in the evaluation tasks; received 
various professional recognition ranging from top-conference Best Demo 
Paper Award to prestigious academic scholarships; organized timely and 
relevant tutorials at KDD'22 and AACL'22; served in TA role for three 
graduate-level CS courses attended in total by ~1000 students; and 
mentored many junior students who continue on to pursue successful 
graduate careers.