Apple's Xcode now has direct integration with the Claude Agent SDK, giving developers the full functionality of Claude Code for building on Apple platforms, from iPhone to Mac to Apple Vision Pro.
Read more: https://t.co/fyZ10bhkN3
@max_glockner@xiangjiangx@leonardoribeiro@IGurevych 📊 Our experiments with multiple LLMs show significant gaps in evidence-based reasoning. 🚧 NeoQA exposes limitations in multi-hop reasoning and shortcut reliance—crucial insights for building trustworthy AI. 🛡️
🚨In the age of information overload, summarizing key info from diverse modalities such as text and images is crucial. But how to do this effectively? Our #ACL2024 paper introduces a self-refinement approach for improving multimodal LLM summaries and REFINESumm, a new dataset 🧵
Don't miss the CCSum presentation at #NAACL2024. We present a large-scale dataset (1.3M) for abstractive news summarization. More factual and informative than summaries in CNN/DM, XSum & Multi-News! Joint work w/ Xiang Jiang
💻 https://t.co/lV34GnxB9d
📃 https://t.co/DUeXWnQiXm
Happy new year! Just a reminder to everyone who works on language modeling that COLM is a new conference that I am co-organizing with several esteemed colleagues from academia and industry. Abstract deadline is on March 22 followed by the paper deadline on March 29.
https://t.co/Fahzql7MfF
I'm thrilled to announce our company, @essential_ai . We believe that breakthroughs in AI will unlock the most profound tools for thought, advancing humanity's collective knowledge and capability.
https://t.co/unA2spJDDR
Exciting work w/ @AdithyaPratapa and @smallcadenza! ✨We generate background summaries for events based on news timelines. https://t.co/gIHCcICX8f Outstanding paper award at #EMNLP2023 summarization track.🤩Check out our dataset for this task: https://t.co/pwzNdxvEtG #NLProc
Delighted to share that our @emnlpmeeting work on background summarization received an outstanding paper award in the summarization track 🏆
w/ Kevin Small and @markusdr
Paper: https://t.co/57LDXHMgDn
Github: https://t.co/kRz7JSlnsw
Highlights below, (1/4) #EMNLP2023
#EMNLP2023 is coming up. Don't miss our paper on controllable text generation! https://t.co/RPXmfAzFO0
We control text readability using RL/PPO with Gaussian rewards, look-ahead decoding, and prompt instructions.
#NLProc@mohitban47@leonardoribeiro
🚨How to generate summaries w/ fine-grained readability-level control?
In our new #EMNLP2023 main paper, we develop 3 readability-control methods via instruction-prompting, RL, & lookahead decoding.
https://t.co/O63y3uqnf4
@mohitban47@markusdr@AmazonScience @uncnlp
🧵👇
In our paper, we propose metrics that combine abstractiveness and factuality. The metrics adjust for different levels of abstractiveness when comparing the factuality of different text generation systems. https://t.co/9JZVmH28uE
Check out our paper "Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization" at #EACL2023 Findings! #NLProc
https://t.co/9JZVmH28uE
Dataset release: 14k human factuality judgements!
cc: @ravisujith, @AmazonScience
🧵👇 1/n
The tradeoff between abstractiveness and factuality is similar to the tradeoff between precision and recall. High precision can be trivially achieved with low recall, just as high factuality can be achieved with low abstractiveness. #NLProc