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#naturallanguageprocessing

2 posts2 participants0 posts today
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Christof Schöch

After the conference & day trip, it was time for meetings. We did plenty! I took part in delegation meetings with #AcademiaSinica and #NTU (National Taiwan Univ.), but there were many more. We learned about exchange opportunities, memoranda were signed, and we presented each others' research priorities and focus areas, and found lots of overlap! #Biodiversity #WorldLiterature #Sinology #DigitalHumanities #ArtifcialIntelligence #NaturalLanguageProcessing, to name just a few. #tcdh #UTrier

A colleague from Academia Sinica presenting on exchange programs, here opportunities for Taiwanese colleagues wanting to go abroad.
One particular building on the campus of Academia sinica, in red brick with a rotunda building, and with a lush green landscape around, including some palm trees.
A beautiful, large, intensely green and somewhat umbrella-shaped tree on the park-like campus of NTU.
Slide in tones of blue from a presentation at #NTU about their research focus areas, among them "Digital Arts and Humanities" and "Sustainable Science". Also, their number of publications: 55,700 and citations: 1,079,700.
Public
Hacker News

UCSD: Large Language Models Pass the Turing Test

arxiv.org/abs/2503.23674

arXiv.orgLarge Language Models Pass the Turing TestWe evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomised, controlled, and pre-registered Turing tests on independent populations. Participants had 5 minute conversations simultaneously with another human participant and one of these systems before judging which conversational partner they thought was human. When prompted to adopt a humanlike persona, GPT-4.5 was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant. LLaMa-3.1, with the same prompt, was judged to be the human 56% of the time -- not significantly more or less often than the humans they were being compared to -- while baseline models (ELIZA and GPT-4o) achieved win rates significantly below chance (23% and 21% respectively). The results constitute the first empirical evidence that any artificial system passes a standard three-party Turing test. The results have implications for debates about what kind of intelligence is exhibited by Large Language Models (LLMs), and the social and economic impacts these systems are likely to have.
#HackerNews#UCSD#LLM
Public
Alexander Janßen

Everyone should have a pet-OCD, and my obsession is esoteric encoding schemes. That kept me awake last night, and now I'm giving in.

Part 1 of a handful; from idea to product, I present you "How to convert arbitrary binary data into English sentences and back again."

Part 2 in the making.

blog.ynfonatic.de/software/202

Alexander W. Janssen’s blog · A PGP Words variant creating natural sentences - part 1This is part 1 of a series of articles I am planning write. In this part I will outline the problem, and offer a solution proposal and define the requirements.
#Mnemonics#Encoding#NaturalLanguageProcessing
Public
ALTA

#Introduction

Hi everyone! It's ALTA here - the Australasian Language Technology Association.

We run an @aclmeeting - aligned workshop in Australia or Aotearoa New Zealand every year, focused on #nlp #NaturalLanguageProcessing.

This year's workshop will be held at @ANUResearch in the beautiful city of #Canberra #CBR on #Ngunnawal land, in early December 2024.

We're a pretty friendly and easy going bunch of folks - and we'd love to connect!

#linguistics#ACL#research
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Victoria Stuart 🇨🇦 🏳️‍⚧️

... cont'd 5/5

Schaeffer et. al. argue that the emergent abilities are predictably acquired according to a smooth scaling law.

Are Emergent Abilities of Large Language Models a Mirage?
arxiv.org/abs/2304.15004

Prompt engineering: Chain-of-thought reasoning:
en.wikipedia.org/wiki/Prompt_e

arXiv.orgAre Emergent Abilities of Large Language Models a Mirage?Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability, appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher's choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous predictable changes in model performance. We present our alternative explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test and confirm three predictions on the effect of metric choice using the InstructGPT/GPT-3 family on tasks with claimed emergent abilities; (2) make, test and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench; and (3) show to choose metrics to produce never-before-seen seemingly emergent abilities in multiple vision tasks across diverse deep networks. Via all three analyses, we provide evidence that alleged emergent abilities evaporate with different metrics or with better statistics, and may not be a fundamental property of scaling AI models.
#LLM#LargeLanguageModels#emergence
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