research

2026

1. Simplifying the Modeling of Arbitrary Conditionals in Natural Language

   Yinhan Lu^*, Eric Elmoznino^*, Léo Gagnon, Sarthak Mittal, Tejas Kasetty, and Guillaume Lajoie

   Jun 2026

   Abs arXiv Bib

   Causal Transformers model sequences through an autoregressive factorization of the joint distribution, which enables efficient left-to-right decoding and conditional likelihood computation. However, they cannot tractably sample from or evaluate arbitrary conditionals – e.g., a block of text conditioned on past and future tokens. Recent work aims to solve this problem through novel architectures, but they often lead to sub-optimal modeling of such conditionals and degraded generations. We propose Arbitrary Conditionals GPT (AC-GPT) which introduces a simple modification to standard causal Transformers to enable evaluating and sampling from arbitrary conditionals – including past, future, and mixed contexts – within a single forward pass. Unlike prior approaches, our method preserves the standard left-to-right ordering and next-token prediction objective essential for both strong performance and efficient training on natural language. Crucially, this compatibility allows existing LLMs to be fine-tuned for arbitrary conditioning. Our empirical results indicate that our method outperforms baselines on modeling arbitrary conditionals, without degrading standard left-to-right performance.

   @misc{lu2026arbitraryconditionals,
     title = {Simplifying the Modeling of Arbitrary Conditionals in Natural Language},
     author = {Lu, Yinhan and Elmoznino, Eric and Gagnon, Léo and Mittal, Sarthak and Kasetty, Tejas and Lajoie, Guillaume},
     year = {2026},
     month = jun,
     eprint = {2606.14943},
     archiveprefix = {arXiv},
     primaryclass = {cs.CL},
   }

2. An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages

   Yinhan Lu^*, Gaganpreet Jhajj^*, Chen Zhang, Anietie Andy, and David Ifeoluwa Adelani

   Apr 2026

   Abs arXiv Bib

   In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepresented in pre-training. Recent work on many-shot ICL suggests that modern LLMs can further benefit from larger ICL examples enabled by their long context windows. However, such gains depend on careful example selection, and the inference cost can be prohibitive for low-resource language communities. In this paper, we present an empirical study of many-shot ICL for machine translation from English into ten truly low-resource languages recently added to FLORES+. We analyze the effects of retrieving more informative examples, using out-of-domain data, and ordering examples by length. Our findings show that many-shot ICL becomes more effective as the number of examples increases. More importantly, we show that BM25-based retrieval substantially improves data efficiency: 50 retrieved examples roughly match 250 many-shot examples, while 250 retrieved examples perform similarly to 1,000 many-shot examples. We further show that ICL provides additional gains on top of fine-tuning.

   @misc{lu2026manyshotict,
     title = {An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages},
     author = {Lu, Yinhan and Jhajj, Gaganpreet and Zhang, Chen and Andy, Anietie and Adelani, David Ifeoluwa},
     year = {2026},
     month = apr,
     eprint = {2604.02596},
     archiveprefix = {arXiv},
     primaryclass = {cs.CL},
   }