Update DLAB

data/DLAB/projects.yaml

@@ -31,7 +31,7 @@ projects:
         title: Privacy-Preserving Distributed Learning with Secret Gradient Descent
         url: https://arxiv.org/pdf/1906.11993.pdf
     date_added: 2021-11-05
-    date_updated: 2022-07-04
+    date_updated: 2024-04-16
     maturity: 1
 
   invariant-language-models:
@@ -55,7 +55,7 @@ projects:
     tags:
       - Natural Language
     date_added: 2021-11-05
-    date_updated: 2022-07-04
+    date_updated: 2024-04-16
 
   eighenthemes:
     name: Eigenthemes
@@ -86,7 +86,7 @@ projects:
             text: EMNLP 2021
             url: https://2021.emnlp.org/
     date_added: 2021-11-05
-    date_updated: 2022-07-04
+    date_updated: 2024-04-16
 
   quotebank:
     name: Quotebank
@@ -119,7 +119,7 @@ projects:
         title: Quotebank
         url: https://quotebank.dlab.tools
     date_added: 2022-09-28
-    date_updated: 2022-09-28
+    date_updated: 2024-04-16
 
   dipps:
     name: DiPPS
@@ -149,7 +149,7 @@ projects:
         title: Differentially Private Propensity Scores for Bias Correction
         url: https://arxiv.org/abs/2210.02360
     date_added: 2023-03-16
-    date_updated: 2023-03-16
+    date_updated: 2024-04-16
 
   genie:
     name: GenIE
@@ -168,15 +168,15 @@ projects:
     code:
       type: Lab GitHub
       url: https://github.com/epfl-dlab/GenIE
-      date_last_commit: 2023-02-23
+      date_last_commit: 2023-03-28
     language: Python
     license: MIT
     information:
       - type: Paper
         title: "GenIE: Generative Information Extraction"
         url: https://arxiv.org/abs/2112.08340
     date_added: 2023-03-16
-    date_updated: 2023-03-16
+    date_updated: 2024-04-16
 
   synthie:
     name: SynthIE
@@ -191,12 +191,123 @@ projects:
     code:
       type: Lab GitHub
       url: https://github.com/epfl-dlab/SynthIE
-      date_last_commit: 2023-03-08
+      date_last_commit: 2023-05-27
     language: Python
     license: MIT
     information:
       - type: Paper
         title: "Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction"
         url: https://arxiv.org/abs/2303.04132
     date_added: 2023-03-16
-    date_updated: 2023-03-16
+    date_updated: 2024-04-16
+
+  aiflows:
+    name: aiFlows
+    description: >
+      Modular AI collaboration framework
+    type: "Framework"
+    categories:
+      - "Learning"
+    applications:
+      - "Info"
+    tags:
+      - Machine Learning
+      - Cloud
+      - Protocol
+    layman_desc: >
+      aiFlows simplifies the design and implementation of complex workflows involving humans, 
+      AI systems, and tools.  
+      It enables modularity by allowing Flows to be stacked like LEGO blocks, reusability by 
+      sharing Flows on the FlowVerse, remote peer-to-peer collaboration between Flows, and 
+      concurrent execution of multiple Flows.  
+      The goal is to empower researchers and practitioners with complete control and 
+      customizability over their AI workflows.
+    tech_desc: >
+      aiFlows is a framework centered around Flows and messages.  
+      Flows are independent, self-contained computational building blocks that can complete 
+      semantically meaningful units of work.  
+      Flows communicate via a standardized message-based interface, enabling modularity, 
+      reusability, remote peer-to-peer collaboration, and concurrency.
+    url: https://epfl-dlab.github.io/aiflows/docs/built_with_sphinx/html/index.html
+    code:
+      type: Lab Github
+      url: https://github.com/epfl-dlab/aiflows?tab=readme-ov-file
+      date_last_commit: 2024-04-12
+    language: Python
+    license: MIT
+    date_added: 2024-04-16
+
+  transformers-cfg:
+    name: Transformers CFG
+    description: >
+      Grammar-constrained text generation with Transformers models
+    type: "Library"
+    categories:
+      - "Learning"
+    applications:
+      - "Info"
+    tags:
+      - Machine Learning
+      - Natural Language
+    layman_desc: >
+      The transformers_cfg library allows you to control the output of
+      language models like GPT-3 by providing a set of rules (grammar) that
+      the generated text must follow.  
+      This is useful for generating structured data like code, JSON
+      objects, or any text that needs to conform to specific patterns or
+      rules.  
+      The library works with popular language models and provides an
+      easy way to incorporate grammar constraints into the text generation
+      process without modifying the underlying models.
+    tech_desc: >
+      Transformers_cfg is an extension library for the Hugging Face
+      Transformers library that enables grammar-constrained text generation.  
+      It provides tools and functionalities to work with context-free
+      grammars (CFGs) for natural language processing tasks involving CFGs.  
+      The library supports various Transformer models, including LLaMa,
+      GPT, Bloom, Mistral, and Falcon, and offers features like multilingual
+      grammar support and integration with Text-Generation-WebUI.
+    code:
+      type: Lab Github
+      url: https://github.com/epfl-dlab/transformers-CFG
+      date_last_commit: 2024-04-13
+    language: Python
+    license: MIT
+    date_added: 2024-04-16
+
+  multilingual-entity-insertion:
+    name: "Entity Insertion in Wikipedia"
+    description: >
+      Multilingual entity insertion in Wikipedia articles
+    type: "Experiments"
+    categories:
+      - "Learning"
+    applications:
+      - "Info"
+    tags:
+      - Machine Learning
+      - Natural Language
+    layman_desc: >
+      Automatically adding relevant links to entities in Wikipedia articles
+      across different languages is a challenging task. This project provides
+      a solution by processing data from Wikipedia dumps and training machine
+      learning models. The data processing extracts information like articles,
+      links, and mentions from the dumps. The modeling code trains models to
+      rank candidate text spans for inserting an entity link. The models are
+      evaluated against various baselines like keyword matching and language
+      models. This helps in improving the quality and consistency of Wikipedia
+      by suggesting relevant entity links across multiple languages.
+    tech_desc: >
+      Proposes a framework for inserting entities into Wikipedia articles
+      across multiple languages. It processes Wikipedia dumps to extract
+      data and train models for entity insertion. The key components are:
+      1) Data processing pipeline to extract relevant data from Wikipedia dumps.
+      2) Modeling code for training entity insertion models using a ranking
+      loss or pointwise loss. 3) Benchmarking code to evaluate models against
+      baselines like BM25, EntQA, and GPT language models.
+    code:
+      type: Lab Github
+      url: https://github.com/epfl-dlab/multilingual-entity-insertion
+      date_last_commit: 2024-04-15
+    language: Jupyter Notebook
+    date_added: 2024-04-16

data/labs.yaml

@@ -76,6 +76,9 @@ labs:
       applying algorithms and techniques in areas including social and information network analysis, machine learning, computational social
       science, data mining, natural language processing, and human computation.
     url: https://dlab.epfl.ch/
+    information:
+      - title: Github Repository
+        url: https://github.com/epfl-dlab
 
   DSLAB:
     name: Dependable Systems Lab

updates/2024-big-update.md

@@ -371,6 +371,10 @@ Linus
   - Updated github project dates
   - Sent email on 2024-04-16
 - DLAB - Robert West
+  - Added aiFlows, transformers-cfg, multilingual-entity-insertion (no license)
+  - Lab webpage errors
+    - on https://dlab.epfl.ch/, `Swiss Data Science Center': `https://datascience.ch/project/the-human-measurement-project-hmp/'
+  - Sent email on 2024-04-16
 
 [//]: # " Labs w/o code - to be checked next time around "