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46 papers · 1 filter
Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?
Arzu Burcu Güven, Anna Rogers, Rob van der Goot
We examine the syntactic properties of BabyLM corpus, and age-groups within CHILDES. While we find that CHILDES does not exhibit strong syntactic differentiation by age, we show th…
The meaning of prompts and the prompts of meaning: Semiotic reflections and modelling
Martin Thellefsen, Amalia Nurma Dewi, Bent Sorensen
This paper explores prompts and prompting in large language models (LLMs) as dynamic semiotic phenomena, drawing on Peirce's triadic model of signs, his nine sign types, and the Dy…
Can structural correspondences ground real world representational content in Large Language Models?
Iwan Williams
Large Language Models (LLMs) such as GPT-4 produce compelling responses to a wide range of prompts. But their representational capacities are uncertain. Many LLMs have no direct co…
CMCTS: A Constrained Monte Carlo Tree Search Framework for Mathematical Reasoning in Large Language Model
Qingwen Lin, Boyan Xu, Guimin Hu +4
This paper introduces the Constrained Monte Carlo Tree Search (CMCTS) framework to enhance the mathematical reasoning capabilities of Large Language Models (LLM). By incorporating…
Mention Attention for Pronoun Translation
Gongbo Tang, Christian Hardmeier
Most pronouns are referring expressions, computers need to resolve what do the pronouns refer to, and there are divergences on pronoun usage across languages. Thus, dealing with th…
SnakModel: Lessons Learned from Training an Open Danish Large Language Model
Mike Zhang, Max Müller-Eberstein, Elisa Bassignana +1
We present SnakModel, a Danish large language model (LLM) based on Llama2-7B, which we continuously pre-train on 13.6B Danish words, and further tune on 3.7M Danish instructions. A…