MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation

The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users. The intended generation can be expressed in natural language, with the model producing faithful interpretations of text prompts. However, expressing complex or nuanced ideas in text alone can be difficult. To ease image generation, we propose MultiFusion that allows one to express complex and nuanced concepts with arbitrarily interleaved inputs of multiple modalities and languages. MutliFusion leverages pre-trained models and aligns them for integration into a cohesive system, thereby avoiding the need for extensive training from scratch. Our experimental results demonstrate the efficient transfer of capabilities from individual modules to the downstream model. Specifically, the fusion of all independent components allows the image generation module to utilize multilingual, interleaved multimodal inputs despite being trained solely on monomodal data in a single language

Tokenizer Choice For LLM Training: Negligible or Crucial?

The recent success of LLMs has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot. Shedding light on this underexplored area, we conduct a comprehensive study on the influence of tokenizer choice on LLM downstream performance by training 24 mono- and multilingual LLMs at a 2.6B parameter scale, ablating different tokenizer algorithms and parameterizations. Our studies highlight that the tokenizer choice can significantly impact the model's downstream performance, training and inference costs. In particular, we find that the common tokenizer evaluation metrics fertility and parity are not always predictive of model downstream performance, rendering these metrics a questionable proxy for the model's downstream performance. Furthermore, we show that multilingual tokenizers trained on the five most frequent European languages require vocabulary size increases of factor three in comparison to English. While English-only tokenizers have been applied to the training of multi-lingual LLMs, we find that this approach results in a severe downstream performance degradation and additional training costs of up to 68%, due to an inefficient tokenization vocabulary

Synthesis and characterization of PEDOT, an intrinsically conductive polymer

International audiencePoly(3,4-ethylenedioxythiophene) (PEDOT) is a highly valuable polymer material for modern electronics due to its impressive electrical conductivity (up to 1000 S.cm$^{−1}$). Combined with poly(styrenesulfonate) (PSS), PEDOT can beprocessed into thin film for a multitude of applications. However, the introduction of PEDOT in melt-state processes of the plastic industryis still challenging because PEDOT is infusible and only commercially available as highly diluted aqueous solutions. Nevertheless, previous study showed that extrusion processing of PEDOT:PSS solutions with PEO as a melting carrier is possiblebut sophisticated pre/post-treatments are mandatory to maintain high level of electrical conductivity up to 5 S.cm$^{−1}$.In this context, the goal of this study is to synthesize electrically conductive polymeric particles of PEDOT. An oxidative chemical polymerization of EDOT was carried out using two different oxidants: Fe$_2$(SO$_4$)$_3$ and FeCl$_3$. Resulting polymeric particles were studied thanks to SEM observations and resistivity measurements. The influence of parameters such as the ratio (monomer/oxidant), the polymerization time or the use of surfactants on the conductivity of PEDOT particles was studied.Interestingly, as-prepared PEDOT particles display conductivities between 0.1 and 10 S.cm$^{−1}$ without any posttreatment. This result is nearly 1000 times higher than previously reported by Jiang et al. A first explanation can be the presence of the oxidant/surfactant combined with PEDOT particles and acting as a dopant. Besides, comparison between the samples allows us to highlight the impact of the monomer-oxidant ratio on the electrical conductivity of the pellets. To better understand these results, correlations between (i) the polymerization process, (ii) the particles morphology and (iii) final electrical properties are currently being investigated