english grammar
NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models
Xu, Ancheng, Tan, Minghuan, Wang, Lei, Yang, Min, Xu, Ruifeng
Numeral systems and units of measurement are two conjoined topics in activities of human beings and have mutual effects with the languages expressing them. Currently, the evaluation of Large Language Models (LLMs) often involves mathematical reasoning, yet little attention is given to how minor changes in numbers or units can drastically alter the complexity of problems and the performance of LLMs. In this paper, we scrutinize existing LLMs on processing of numerals and units of measurement by constructing datasets with perturbations. We first anatomize the reasoning of math word problems to different sub-procedures like numeral conversions from language to numbers and measurement conversions based on units. Then we further annotate math word problems from ancient Chinese arithmetic works which are challenging in numerals and units of measurement. Experiments on perturbed datasets demonstrate that LLMs still encounter difficulties in handling numeral and measurement conversions.
- North America > United States > Minnesota > Hennepin County > Minneapolis (0.14)
- Asia > China > Guangdong Province > Shenzhen (0.04)
- Europe > Italy > Tuscany > Florence (0.04)
- (8 more...)
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation
He, Yutong, Robey, Alexander, Murata, Naoki, Jiang, Yiding, Williams, Joshua, Pappas, George J., Hassani, Hamed, Mitsufuji, Yuki, Salakhutdinov, Ruslan, Kolter, J. Zico
Prompt engineering is effective for controlling the output of text-to-image (T2I) generative models, but it is also laborious due to the need for manually crafted prompts. This challenge has spurred the development of algorithms for automated prompt generation. However, these methods often struggle with transferability across T2I models, require white-box access to the underlying model, and produce non-intuitive prompts. In this work, we introduce PRISM, an algorithm that automatically identifies human-interpretable and transferable prompts that can effectively generate desired concepts given only black-box access to T2I models. Inspired by large language model (LLM) jailbreaking, PRISM leverages the in-context learning ability of LLMs to iteratively refine the candidate prompts distribution for given reference images. Our experiments demonstrate the versatility and effectiveness of PRISM in generating accurate prompts for objects, styles, and images across multiple T2I models, including Stable Diffusion, DALL-E, and Midjourney.
- Europe > France > Occitanie > Haute-Garonne > Toulouse (0.04)
- South America > Colombia > Meta Department > Villavicencio (0.04)
- North America > United States > Washington > King County > Seattle (0.04)
- (5 more...)
- Health & Medicine (0.92)
- Leisure & Entertainment (0.67)
- Transportation > Air (0.60)
- Information Technology > Sensing and Signal Processing > Image Processing (1.00)
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Large Language Model (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning > Generative AI (0.50)
Grammatical Bigrams
Unsupervised learning algorithms have been derived for several sta(cid:173) tistical models of English grammar, but their computational com(cid:173) plexity makes applying them to large data sets intractable. This paper presents a probabilistic model of English grammar that is much simpler than conventional models, but which admits an effi(cid:173) cient EM training algorithm. The model is based upon grammat(cid:173) ical bigrams, i.e., syntactic relationships between pairs of words. We present the results of experiments that quantify the represen(cid:173) tational adequacy of the grammatical bigram model, its ability to generalize from labelled data, and its ability to induce syntactic structure from large amounts of raw text.
Language-independence of DisCoCirc's Text Circuits: English and Urdu
Waseem, Muhammad Hamza, Liu, Jonathon, Wang-Maścianica, Vincent, Coecke, Bob
DisCoCirc is a newly proposed framework for representing the grammar and semantics of texts using compositional, generative circuits. While it constitutes a development of the Categorical Distributional Compositional (DisCoCat) framework, it exposes radically new features. In particular, [14] suggested that DisCoCirc goes some way toward eliminating grammatical differences between languages. In this paper we provide a sketch that this is indeed the case for restricted fragments of English and Urdu. We first develop DisCoCirc for a fragment of Urdu, as it was done for English in [14]. There is a simple translation from English grammar to Urdu grammar, and vice versa. We then show that differences in grammatical structure between English and Urdu - primarily relating to the ordering of words and phrases - vanish when passing to DisCoCirc circuits.
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
Eida: English Coach - Apps on Google Play
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