Canada

AAAI 2024

Implications of LLMs

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Recently, the rise of large-scale vision-language pretrained models like CLIP, coupled with the technology of Parameter-Efficient FineTuning (PEFT), has captured substantial attraction in video action recognition. Nevertheless, prevailing approaches tend to prioritize strong supervised performance at the expense of compromising the models' generalization capabilities during transfer. In this paper, we introduce a novel Multimodal, Multi-task CLIP adapting framework named M2-CLIP to address these challenges, preserving both high supervised performance and robust transferability.
Firstly, to enhance the individual modality architectures, we introduce multimodal adapters to both the visual and text branches. Specifically, we design a novel visual TED-Adapter, that performs global Temporal Enhancement and local temporal Difference modeling to improve the temporal representation capabilities of the visual encoder. Moreover, we adopt text encoder adapters to strengthen the learning of semantic label information.
Secondly, we design a multi-task decoder with a rich set of supervisory signals, including the original contrastive learning head, a cross-modal classification head, a cross-modal masked language modeling head, and a visual classification head. This multi-task decoder adeptly satisfies the need for strong supervised performance within a multimodal framework.
Experimental results validate the efficacy of our approach, demonstrating exceptional performance in supervised learning while maintaining strong generalization in zero-shot scenarios.

A Multimodal, Multi-Task Adapting Framework for Video Action Recognition

Large language models (LLMs) have demonstrated impressive proficiency in information retrieval, while they are prone to generating incorrect responses that conflict with reality, a phenomenon known as intrinsic hallucination. The critical challenge lies in the unclear and unreliable fact distribution within LLMs trained on vast amounts of data. The prevalent approach frames the factual detection task as a question-answering paradigm, where the LLMs are asked about factual knowledge and examined for correctness. However, existing studies primarily focused on deriving test cases only from several specific domains, such as movies and sports, limiting the comprehensive observation of missing knowledge and the analysis of unexpected hallucinations. To address this issue, we propose OntoFact, an adaptive framework for detecting unknown facts of LLMs, devoted to mining the ontology-level skeleton of the missing knowledge. Specifically, we argue that LLMs could expose the ontology-based similarity among missing facts and introduce five representative knowledge graphs (KGs) as benchmarks. We further devise a sophisticated ontology-driven reinforcement learning (ORL) mechanism to produce error-prone test cases with specific entities and relations automatically. The ORL mechanism rewards the KGs for navigating toward a feasible direction for unveiling factual errors. Moreover, empirical efforts demonstrate that dominant LLMs are biased towards answering Yes rather than No, regardless of whether this knowledge is included. To mitigate the overconfidence of LLMs, we leverage a hallucination-free detection (HFD) strategy to tackle unfair comparisons between baselines, thereby boosting the result robustness. Experimental results on 5 datasets, using 32 representative LLMs, reveal a general lack of fact in current LLMs. Notably, ChatGPT exhibits fact error rates of 51.6% on DBpedia and 64.7% on YAGO, respectively. Additionally, the ORL mechanism demonstrates promising error prediction scores, with F1 scores ranging from 70% to 90% across most LLMs. Compared to the exhaustive testing, ORL achieves an average recall of 80% while reducing evaluation time by 35.29% to 63.12%.

OntoFact: Unveiling Fantastic Fact-Skeleton of LLMs via Ontology-Driven Reinforcement Learning

Sound Event Detection (SED) aims to predict the temporal boundaries of all the events of interest and their class labels, given an unconstrained audio sample. Taking either the split-and-classify (i.e., frame-level) strategy or the more principled event-level modeling approach, all existing methods consider the SED problem from the discriminative learning perspective. In this work, we reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process, conditioned on a target audio sample. During training, our model learns to reverse the noising process by converting noisy latent queries to the ground-truth versions in the elegant Transformer decoder framework. Doing so enables the model generate accurate event boundaries from even noisy queries during inference. Extensive experiments on the Urban-SED and EPIC-Sounds datasets demonstrate that our model significantly outperforms existing alternatives, with 40+% faster convergence in training. Code: https://github.com/Surrey-UPLab/DiffSED

DiffSED: Sound Event Detection with Denoising Diffusion

Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To address this issue, the continual knowledge graph embedding (CKGE) task has been proposed to train the KGE model by learning emerging knowledge efficiently while simultaneously preserving decent old knowledge. However, the explicit graph structure in KGs, which is critical for the above goal, has been heavily ignored by existing CKGE methods. On the one hand, existing methods usually learn new triples in a random order, destroying the inner structure of new KGs. On the other hand, old triples are preserved with equal priority, failing to alleviate catastrophic forgetting effectively. In this paper, we propose a competitive method for CKGE based on incremental distillation (IncDE), which considers the full use of the explicit graph structure in KGs. First, to optimize the learning order, we introduce a hierarchical strategy, ranking new triples for layer-by-layer learning. By employing the inter- and intra-hierarchical orders together, new triples are grouped into layers based on the graph structure features. Secondly, to preserve the old knowledge effectively, we devise a novel incremental distillation mechanism, which facilitates the seamless transfer of entity representations from the previous layer to the next one, promoting old knowledge preservation. Finally, we adopt a two-stage training paradigm to avoid the over-corruption of old knowledge influenced by under-trained new knowledge. Experimental results demonstrate the superiority of IncDE over state-of-the-art baselines. Notably, the incremental distillation mechanism contributes to improvements of 0.2\%-6.5\% in the mean reciprocal rank (MRR) score. More exploratory experiments validate the effectiveness of IncDE in proficiently learning new knowledge while preserving old knowledge across all time steps.

Towards Continual Knowledge Graph Embedding via Incremental Distillation

In recent years, the rapid advancement of artificial intelligence (AI) has fostered an urgent need to better prepare current and future educators to be able to integrate AI technologies in their teaching and to teach AI literacy to PreK-12 students. While many organizations have developed professional learning opportunities for inservice educators, a gap remains for resources specifically designed for those facilitating and enrolled in Educator Preparation Programs (EPPs). In response to this gap, the International Society for Technology in Education (ISTE) launched its first AI Explorations for EPPs Faculty Fellowship. As a result of the Faculty Fellows’ collaboration, this paper articulates a framework of seven critical strategies with the potential to address the urgent need EPPs have in preparing preservice teachers to effectively integrate AI-powered instructional tools and to teach this new area of content knowledge in PreK-12 classrooms. In addition, we provide a review of literature and an overview of the emerging needs for integrating AI education in EPPs. We demonstrate why support for preservice teachers’ critical examination and application of AI, including a focus on the issues of equity, ethics, and culturally responsive teaching, is essential to their later success in PreK-12 classrooms. Recommendations for further research and learning are also provided to promote community-wide initiatives for supporting the integration of AI in education through Educator Preparation Programs and beyond.

A Framework for Approaching AI Education in Educator Preparation Programs | VIDEO

High school teachers from many disciplines have growing interests in teaching about artificial intelligence (AI). This cross-disciplinary interest reflects the prevalence of AI tools across society, such as Generative AI tools built upon Large Language Models (LLM). However, high school classes are unique and complex environments, led by teachers with limited time and resources with priorities that vary by class and the students they serve. Therefore, developing curricula about AI for classes that span many disciplines (e.g. history, art, math) must involve centering the expertise of cross-disciplinary teachers. In this study, we conducted five collaborative curricular co-design sessions with eight teachers who taught high school humanities and STEM classes. We sought to understand how teachers considered AI when it was taught in art, math, and social studies contexts, as well as opportunities and challenges they identified with incorporating AI tools into their instruction. We found that teachers considered technical skills and ethical debates around AI, opportunities for "dual exploration" between AI and disciplinary learning, and limitations of AI tools as supporting engagement and reflection but also potentially distracting. We interpreted our findings relative to co-designing adaptable AI curricula to support teaching about and with AI across high school disciplines.

Co-designing AI Education Curriculum with Cross-Disciplinary High School Teachers | VIDEO

As Artificial Intelligence (AI) continues to permeate various aspects of societies, understanding the disparities in AI knowledge and skills across different living areas becomes imperative. This study investigates the digital and AI skills gaps in the context of small and large European living areas, shedding light on the potential hindrances to unleashing the full economic and social potentials of these regions in an AI-enabled economy.
Small living areas have emerged as significant contributors to Europe's economy, offering an alternative to the bustling environment of larger cities for those seeking an improved quality of life. Nonetheless, they often encounter challenges related to digital infrastructure, access to financial resources, and digital skills gaps, limiting their economic and social growth prospects.
Drawing from a comprehensive dataset encompassing over 4,000 respondents across eight EU countries, this research examines the current perceptions and understandings of AI and digital skills within two distinct population groups: residents of smaller living areas and their counterparts in larger communities. Through bivariate analysis, notable insights are revealed concerning trust in AI solutions and entities, self-assessed digital skills, AI awareness, and attitudes in both population groups.
The findings indicate that individuals residing in smaller living areas exhibit lower trust in AI solutions and entities, alongside diminished self-perceived digital skills in comparison to those in larger communities. Notably, no significant disparities are observed in AI awareness, attitude, and self-assessed AI skills between the two groups.
Further examination of key demographic variables, including age, gender, and education, underscores intriguing relationships between these variables and AI-related perceptions and skills. In living areas with populations less than 30,000, digital skills exhibit associations with all measured variables, while gender and education correlate with specific facets of trust in AI solutions and entities. Age groups demonstrate connections solely with trust in AI solutions and entities, whereas education reveals relationships with all variables except trust in AI solutions.
On the other hand, in larger living areas, where populations are of more than 30,000, digital skills exhibit similar relationships with all measured variables, and gender again influences trust in AI solutions. Interestingly, age groups exclusively correlate with AI attitudes and trust in AI solutions and entities, omitting a link to AI awareness. Additionally, education displays connections with all variables except trust in AI solutions, mirroring patterns observed in smaller communities.
These insights underscore the significance of addressing digital and AI skills gaps in fostering growth and preparedness for the AI-driven future. As AI becomes increasingly integral to various aspects of society, targeted interventions and policies are essential to bridge these gaps and enable individuals and communities to harness the transformative potential of AI-enabled economies.

Addressing Digital and AI Skills Gaps in European Living Areas: A Comparative Analysis of Small and Large Communities

Understanding student behavior in educational settings is critical in improving both the quality of pedagogy and the level of student engagement. While various AI-based models exist for classroom analysis, they tend to specialize in limited tasks and lack generalizability across diverse educational environments. Additionally, these models often fall short in ensuring student privacy and in providing actionable insights accessible to educators. To bridge this gap, we introduce a unified, end-to-end framework by leveraging temporal action detection techniques and advanced large language models for a more nuanced student behavior analysis. Our proposed framework provides an end-to-end pipeline that starts with raw classroom video footage and culminates in the autonomous generation of pedagogical reports. It offers a comprehensive and scalable solution for student behavior analysis. Experimental validation confirms the capability of our framework to accurately identify student behaviors and to produce pedagogically meaningful insights, thereby setting the stage for future AI-assisted educational assessments.

From Raw Video to Pedagogical Insights: A Unified Framework for Student Behavior Analysis

This is the New Faculty Highlights application for Xinya Du.
It includes the cover letter, CV, and statement for the research talk.

Making Natural Language Reasoning Explainable and Faithful

The attached file contains my CV, research statement, and cover letter for application.

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