Evaluating AI Safety Through Local Policy: Findings from the UbuntuGuard benchmark A new paper introducing the UbuntuGuard benchmark questions whether strong results on English-language safety tests consistently translate into responsible use. Built from policies developed by 155 Af...

AI safety isn’t universal.

#UbuntuGuard, led by researchers at @brownpublichealth.bsky.social and collaborators, shows that models that pass English benchmarks can fail when tested against locally grounded policy rules across African languages.

Read more:
rebootdemocracy.ai/blog/ubuntu-...

Tassy24 (Tassallah Abdullahi) User profile of Tassallah Abdullahi on Hugging Face

6/n: We release:
 - The dataset for KG-based inductive reasoning: huggingface.co/Tassy24
- Paper: arxiv.org/abs/2502.13344
- Repo: github.com/rsinghlab/K-Pa….

Big thanks to all my incredible collaborators- this wouldn’t be possible without your brilliance!

5/n: This open doors in biomedical AI:
• Structured KG reasoning + LLMs' generative power = transparent, scientific discovery.
• Imagine helping scientists understand why a treatment works, not just what!

4/n: Why this matters:
• Interpretability baked in - paths double as explanations.
•Efficiency - huge KG reductions help scale to real-world biomedical workloads.
•Generalization - works in unseen (inductive) settings.

3/n: 🔥 Results across model families (LLMs & GNNs):
• Tx-Gemma 27B: +19.8 F1 on interaction severity
• Llama 70B: +8.5 F1 on similar tasks.
• EmerGNN: trains on 90% smaller KG with no loss in accuracy
• That cross-architecture gain is rarely seen and hard to ignore.

2/n: K-Paths is not just a retrieval method.
It’s a pipeline that combines:
• Heuristics path finding (Yen’s algorithm)
• Diversity-aware selection
• Graph pruning
• Natural language transformation to aid reasoning for LLMs.
•GNN/LLM integration

1/n: Most KG methods for drug discovery tasks rely on GNNs or focus on narrow QA tasks.

K-Paths is model-agnostic and reframes KG use for reasoning & discovery, especially in inductive settings (e.g. emerging drugs/diseases).

Thrilled to share our KDD 2025 paper: Can large language models (LLMs) reason like biomedical scientists?

We introduce K-Paths, a retrieval framework for extracting reasoning paths from knowledge graphs (KGs) to aid drug discovery tasks.

👇 Thread:

Amazing! Congratulations!!!

Speech tech is everywhere, even healthcare. But how well does it work for African accented English? 🤔
We benchmarked SOTA models for speaker diarization, ASR & LLM summarization on medical & general conversations.
Find me at the 11 am poster session in Hall 3 to learn more!
#NLP4Healthcare

Overview of TREC 2024 Biomedical Generative Retrieval (BioGen) Track With the advancement of large language models (LLMs), the biomedical domain has seen significant progress and improvement in multiple tasks such as biomedical question answering, lay language summariz...

I am pleased to share the report of the Text Retrieval Conference (TREC) Biomedical Generative Retrieval (BioGen) Track, a challenge evaluation to assess reference attribution in LLMs for clinical questions.
arxiv.org/abs/2411.18069

Thank you for responding. I have not done UX work but my research focuses on human-centered AI, exploring how AI can be adapted to better serve human needs across various applications. I’m happy to share more details of my work in a less constrained setting. Here is my resume: tassabdul.github.io

Hi Dr @mihaelav.bsky.social, I am super interested. My research lies at the intersection of NLP and IR aiming to derive factual insights from extensive literature or knowledge graphs to support accurate, reliable, and evidence-based decision-making in various domains. Thanks your consideration.

I’d like to join. Thanks!

Hi Dr @boghuma.bsky.social , I am computer science researcher and I would love to be included in the starter pack. Thanks.

Thank you Dr. @kevinkaichuang.bsky.social for the follow back! As my first follower you deserve a shoutout. Have a great week ahead!