13: RAG Efficiency – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
13: RAG Efficiency – Retrieval Augmented Generation (CS6101 WING.NUS)
2:14:39
12: Source Credibility Assessment – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
12: Source Credibility Assessment – Retrieval Augmented Generation (CS6101 WING.NUS)
2:02:18
11: Graph RAG – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
11: Graph RAG – Retrieval Augmented Generation (CS6101 WING.NUS)
2:17:02
10: Multimodal RAG – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
10: Multimodal RAG – Retrieval Augmented Generation (CS6101 WING.NUS)
1:46:46
09: Bootstrapping and Iterative Retrieval – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
09: Bootstrapping and Iterative Retrieval – Retrieval Augmented Generation (CS6101 WING.NUS)
2:03:12
08: Document Representation – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
08: Document Representation – Retrieval Augmented Generation (CS6101 WING.NUS)
1:58:45
07: RAG Models – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
07: RAG Models – Retrieval Augmented Generation (CS6101 WING.NUS)
1:54:30
06: Ranking and Re-ranking – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
06: Ranking and Re-ranking – Retrieval Augmented Generation (CS6101 WING.NUS)
1:53:10
05: Training: SFT, ICL and Model Scaling – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
05: Training: SFT, ICL and Model Scaling – Retrieval Augmented Generation (CS6101 WING.NUS)
2:06:16
04: Vector Stores – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
04: Vector Stores – Retrieval Augmented Generation (CS6101 WING.NUS)
2:06:02
03: LLM Prompting – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
03: LLM Prompting – Retrieval Augmented Generation (CS6101 WING.NUS)
1:54:02
02: Foundations of Large Language Models – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
02: Foundations of Large Language Models – Retrieval Augmented Generation (CS6101 WING.NUS)
2:04:36
Scholarly Document Processing Research in the Age of AI
Web IR / NLP Group at NUS
Scholarly Document Processing Research in the Age of AI
53:33
01: RAG Overview – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
01: RAG Overview – Retrieval Augmented Generation (CS6101 WING.NUS)
2:08:50
00: Introduction and Orientation – Retrieval Augmented Generation (CS6101 WING.NUS)
Web IR / NLP Group at NUS
00: Introduction and Orientation – Retrieval Augmented Generation (CS6101 WING.NUS)
1:55:09
The Boy Who Harnessed the Wind: Getting more out of LLM Prompting
Web IR / NLP Group at NUS
The Boy Who Harnessed the Wind: Getting more out of LLM Prompting
48:18
Rethinking Recommender System's Sustainability and Dataset Selection / Joeran Beel (U. Siegen)
Web IR / NLP Group at NUS
Rethinking Recommender System's Sustainability and Dataset Selection / Joeran Beel (U. Siegen)
55:33
LLMs for Social Simulation: Progress, Opportunities and Challenges
Web IR / NLP Group at NUS
LLMs for Social Simulation: Progress, Opportunities and Challenges
33:56
13: Security and Privacy – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
13: Security and Privacy – Large Language Models (NUS CS6101 NUS.WING)
2:30:06
12: Harms – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
12: Harms – Large Language Models (NUS CS6101 NUS.WING)
2:25:42
11: Multimodal LLMs – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
11: Multimodal LLMs – Large Language Models (NUS CS6101 NUS.WING)
2:25:06
10: Retrieval Based LLMs – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
10: Retrieval Based LLMs – Large Language Models (NUS CS6101 NUS.WING)
2:15:33
09: Transfer Learning – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
09: Transfer Learning – Large Language Models (NUS CS6101 NUS.WING)
1:55:11
WINGmate – An AI Course Recommender Algorithm
Web IR / NLP Group at NUS
WINGmate – An AI Course Recommender Algorithm
2:29
SSID – A Plagiarism Detection System
Web IR / NLP Group at NUS
SSID – A Plagiarism Detection System
2:44
08: Reasoning – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
08: Reasoning – Large Language Models (NUS CS6101 NUS.WING)
2:12:01
CocoSciSum - a scientific summarization toolkit with compositional controllability
Web IR / NLP Group at NUS
CocoSciSum - a scientific summarization toolkit with compositional controllability
2:53
07: Data and Knowledge – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
07: Data and Knowledge – Large Language Models (NUS CS6101 NUS.WING)
2:14:23
06: Scaling Up, Training and Parallelism – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
06: Scaling Up, Training and Parallelism – Large Language Models (NUS CS6101 NUS.WING)
2:11:19
05: Modeling – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
05: Modeling – Large Language Models (NUS CS6101 NUS.WING)
2:22:18
04: Representation Capacity – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
04: Representation Capacity – Large Language Models (NUS CS6101 NUS.WING)
2:11:46
03: Prompting and Zero-Shot Inference – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
03: Prompting and Zero-Shot Inference – Large Language Models (NUS CS6101 NUS.WING)
2:32:56
02: Adaptation – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
02: Adaptation – Large Language Models (NUS CS6101 NUS.WING)
2:09:31
01: What are Large Language Models – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
01: What are Large Language Models – Large Language Models (NUS CS6101 NUS.WING)
13:18
00: Introduction and Orientation – Large Language Models (NUS CS6101 NUS.WING)
Web IR / NLP Group at NUS
00: Introduction and Orientation – Large Language Models (NUS CS6101 NUS.WING)
46:42
Introduction video for CocoSciSum
Web IR / NLP Group at NUS
Introduction video for CocoSciSum
2:23
A Hitchhiker's Guide to Ontology / Fabian Suchanek (Télécom Paris)
Web IR / NLP Group at NUS
A Hitchhiker's Guide to Ontology / Fabian Suchanek (Télécom Paris)
54:04
Grace Hui Yang @ NSSDM. High-Quality Diversification for Task-Oriented Dialogue Systems.
Web IR / NLP Group at NUS
Grace Hui Yang @ NSSDM. High-Quality Diversification for Task-Oriented Dialogue Systems.
48:37
Xiang Wang @ NSSDM. Explainability of Graph Neural Networks.
Web IR / NLP Group at NUS
Xiang Wang @ NSSDM. Explainability of Graph Neural Networks.
33:35
Chirag Shah @ NSSDM. What does Fairness in Information Access Mean and Can We Achieve It?
Web IR / NLP Group at NUS
Chirag Shah @ NSSDM. What does Fairness in Information Access Mean and Can We Achieve It?
44:22
Chenliang Li @ NSSDM. Recent Advances in Candidate Matching.
Web IR / NLP Group at NUS
Chenliang Li @ NSSDM. Recent Advances in Candidate Matching.
27:43
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing-COLING22
Web IR / NLP Group at NUS
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing-COLING22
1:12:24
PM K-LightGCN: Optimizing for Accuracy and Popularity Match in Course Recommendation
Web IR / NLP Group at NUS
PM K-LightGCN: Optimizing for Accuracy and Popularity Match in Course Recommendation
7:19
Hands-On Class with HuggingFace 🤗: Build Machine Learning demonstrations with Gradio (Omar Espejel)
Web IR / NLP Group at NUS
Hands-On Class with HuggingFace 🤗: Build Machine Learning demonstrations with Gradio (Omar Espejel)
1:02:49
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking [ACL 2022]
Web IR / NLP Group at NUS
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking [ACL 2022]
5:59
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (Short) – 2020
Web IR / NLP Group at NUS
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (Short) – 2020
10:39
Transformer Memory as a Differentiable Search Index / Yi Tay (Google Research)
Web IR / NLP Group at NUS
Transformer Memory as a Differentiable Search Index / Yi Tay (Google Research)
48:46
The curious case of self-training: from vision to language and beyond / Thang Luong (Google Brain)
Web IR / NLP Group at NUS
The curious case of self-training: from vision to language and beyond / Thang Luong (Google Brain)
47:41
Beyond Sentences & Paragraphs: Towards Document-level & Multi-doc Understanding/Arman Cohan (UW&AI2)
Web IR / NLP Group at NUS
Beyond Sentences & Paragraphs: Towards Document-level & Multi-doc Understanding/Arman Cohan (UW&AI2)
1:05:21
Deep Reinforcement Learning for Recommendation Systems / Xiangyu Zhao (City U, HK)
Web IR / NLP Group at NUS
Deep Reinforcement Learning for Recommendation Systems / Xiangyu Zhao (City U, HK)
58:09
Research Fast and Slow (ICADL/AP-iConference 2021 Keynote)
Web IR / NLP Group at NUS
Research Fast and Slow (ICADL/AP-iConference 2021 Keynote)
1:09:04
Revisiting Few-Shot learning for Natural Language Understanding / Yanan Zheng (Tsinghua University)
Web IR / NLP Group at NUS
Revisiting Few-Shot learning for Natural Language Understanding / Yanan Zheng (Tsinghua University)
1:02:16
Neural Networks with Natural Language Explanations / Oana-Maria Camburu (University of Oxford)
Web IR / NLP Group at NUS
Neural Networks with Natural Language Explanations / Oana-Maria Camburu (University of Oxford)
1:08:06
Graph Representation Learning From Knowledge Graphs to Recommender Systems / Hongwei Wang (UIUC)
Web IR / NLP Group at NUS
Graph Representation Learning From Knowledge Graphs to Recommender Systems / Hongwei Wang (UIUC)
1:06:59
DADgraph: A Discourse-aware Dialogue Graph NN for Multiparty Dialogue Machine Reading Comprehension
Web IR / NLP Group at NUS
DADgraph: A Discourse-aware Dialogue Graph NN for Multiparty Dialogue Machine Reading Comprehension
8:50
Commonsense Knowledge and Reasoning in Natural Language / Vered Shwartz (AI2 & UW)
Web IR / NLP Group at NUS
Commonsense Knowledge and Reasoning in Natural Language / Vered Shwartz (AI2 & UW)
1:06:46
Reliability Testing for Natural Language Processing Systems [ACL 2021]
Web IR / NLP Group at NUS
Reliability Testing for Natural Language Processing Systems [ACL 2021]
11:55
Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots [NAACL 2021]
Web IR / NLP Group at NUS
Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots [NAACL 2021]
7:57
Guarding Against Spurious Correlations in Natural Language Understanding / He He (NYU)
Web IR / NLP Group at NUS
Guarding Against Spurious Correlations in Natural Language Understanding / He He (NYU)
1:07:15
Implicit Representations of Meaning in Neural Language Models / Jacob Andreas (MIT)
Web IR / NLP Group at NUS
Implicit Representations of Meaning in Neural Language Models / Jacob Andreas (MIT)
57:57
Conversations as Knowledge: from Question Answering to Summarization / Jason C.S. Wu (Salesforce)
Web IR / NLP Group at NUS
Conversations as Knowledge: from Question Answering to Summarization / Jason C.S. Wu (Salesforce)
49:03
Text Generation with No (Good) Data: New RL and Causal Frameworks / Zhiting Hu (UCSD)
Web IR / NLP Group at NUS
Text Generation with No (Good) Data: New RL and Causal Frameworks / Zhiting Hu (UCSD)
49:04
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them / Patrick Lewis (UCL / FAIR)
Web IR / NLP Group at NUS
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them / Patrick Lewis (UCL / FAIR)
45:59
Understanding Event Processes in Natural Language / Muhao Chen (USC)
Web IR / NLP Group at NUS
Understanding Event Processes in Natural Language / Muhao Chen (USC)
1:06:16
Multi-modal Cooking Workflow Construction for Food Recipes (ACM MM 2020 full paper)
Web IR / NLP Group at NUS
Multi-modal Cooking Workflow Construction for Food Recipes (ACM MM 2020 full paper)
16:31
COVID-19 Scholarly Document Dashboard
Web IR / NLP Group at NUS
COVID-19 Scholarly Document Dashboard
1:36
ServiceMarq: Extracting Service Contributions from Call for Papers (DocEng'20)
Web IR / NLP Group at NUS
ServiceMarq: Extracting Service Contributions from Call for Papers (DocEng'20)
7:22
Animesh Prasad | Ph.D. Thesis Defence
Web IR / NLP Group at NUS
Animesh Prasad | Ph.D. Thesis Defence
1:05:43
[SSNLP '19] Heng Ji / PaperRobot: Automated Scientific Knowledge Graph Construction & Paper Writing
Web IR / NLP Group at NUS
[SSNLP '19] Heng Ji / PaperRobot: Automated Scientific Knowledge Graph Construction & Paper Writing
1:06:11
Michael Farber / The MS Academic KG: A Linked Data Source with 8B+ Triples of Scholarly Data
Web IR / NLP Group at NUS
Michael Farber / The MS Academic KG: A Linked Data Source with 8B+ Triples of Scholarly Data
47:44
Quasi-experimental methods for social media analysis / Kokil Jaidka
Web IR / NLP Group at NUS
Quasi-experimental methods for social media analysis / Kokil Jaidka
43:30
Emotion Recognition in Conversation / Soujanya Poria
Web IR / NLP Group at NUS
Emotion Recognition in Conversation / Soujanya Poria
50:54
Text Generation, Editing and Summarization / Lidong Bing (Alibaba DAMO)
Web IR / NLP Group at NUS
Text Generation, Editing and Summarization / Lidong Bing (Alibaba DAMO)
1:17:33
ChairViz Conference / Workshop Analytics Web Toolkit
Web IR / NLP Group at NUS
ChairViz Conference / Workshop Analytics Web Toolkit
3:57