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A Comparative Approach to Assessing Linguistic Creativity of Large Language Models and Humans
Anca Dinu, Andra-Maria Florescu, Alina Resceanu
https://arxiv.org/abs/2507.12039

A Comparative Approach to Assessing Linguistic Creativity of Large Language Models and Humans
The following paper introduces a general linguistic creativity test for humans and Large Language Models (LLMs). The test consists of various tasks aimed at assessing their ability to generate new original words and phrases based on word formation processes (derivation and compounding) and on metaphorical language use. We administered the test to 24 humans and to an equal number of LLMs, and we automatically evaluated their answers using OCSAI tool for three criteria: Originality, Elaboration, …

Since halving my #sertraline dose, down to 50mg / day, my brain feels like it’s halved in processing power. Words are more difficult to find, my speech is slower, my thought patterns and creativity are stifled. I’m generally more lethargic. On the plus side I’m less agitated and less prone to angry outbursts. If only I could have all of the good bits and none of the bad. That’s not really h…

Manchmal muss man sich im Tag besonders gut verankern.
Handgemalter #wandklexschmuck, eh klar. Zu finden auf
#miniaturepainting #artShop
#fediArt #mastoArt #creativeToots

stark och gullig halacha yomis (dagligt mail om judisk lag) idag https://oukosher.org/halacha-yomis/my-father-as…

This https://arxiv.org/abs/2505.18492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Enumerate-Conjecture-Prove: Formally Solving Answer-Construction Problems in Math Competitions
Mathematical reasoning lies at the heart of artificial intelligence, underpinning applications in education, program verification, and research-level mathematical discovery. Mathematical competitions, in particular, present two challenging problem types: theorem-proving, requiring rigorous proofs of stated conclusions, and answer-construction, involving hypothesizing and formally verifying mathematical objects. Large Language Models (LLMs) effectively generate creative candidate answers but str…

An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring
Sana Ebrahimi, Mohsen Dehghankar, Abolfazl Asudeh
https://arxiv.org/abs/2505.24239 ht…

An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring
While multi-agent LLM systems show strong capabilities in various domains, they are highly vulnerable to adversarial and low-performing agents. To resolve this issue, in this paper, we introduce a general and adversary-resistant multi-agent LLM framework based on credibility scoring. We model the collaborative query-answering process as an iterative game, where the agents communicate and contribute to a final system output. Our system associates a credibility score that is used when aggregating…

Reconstructing Biological Pathways by Applying Selective Incremental Learning to (Very) Small Language Models
Pranta Saha, Joyce Reimer, Brook Byrns, Connor Burbridge, Neeraj Dhar, Jeffrey Chen, Steven Rayan, Gordon Broderick
https://arxiv.org/abs/2507.04432

Reconstructing Biological Pathways by Applying Selective Incremental Learning to (Very) Small Language Models
The use of generative artificial intelligence (AI) models is becoming ubiquitous in many fields. Though progress continues to be made, general purpose large language AI models (LLM) show a tendency to deliver creative answers, often called "hallucinations", which have slowed their application in the medical and biomedical fields where accuracy is paramount. We propose that the design and use of much smaller, domain and even task-specific LM may be a more rational and appropriate use of this tec…