Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai.

Vocabulary adaptation, which integrates new vocabulary into pre-trained language models (LMs), enables expansion to new languages and mitigates token overfragmentation. However, existing approaches are limited by their reliance on heuristic or external embeddings. We propose VocADT, a novel method for vocabulary adaptation using adapter modules that are trained to learn the optimal linear combination of existing embeddings while keeping the model's weights fixed. VocADT offers a flexible and scalable solution without requiring external resources or language constraints. Across 11 languages--with various scripts, resource availability, and fragmentation--we demonstrate that VocADT outperforms the original Mistral model (Jiang et al., 2023) and other baselines across various multilingual tasks. We find that Latin-script languages and highly fragmented languages benefit the most from vocabulary adaptation. We further finetune the adapted model on the generative task of machine translation and find that vocabulary adaptation is still beneficial after fine-tuning and that VocADT is the most effective method. Vocabulary adaptation (or transfer)--a process of modifying a pre-trained language model (LM) to use a new vocabulary--offers several key advantages.

Large language models (LLMs) have achieved remarkable success across various NLP tasks, yet their focus has predominantly been on English due to Englishcentric pre-training and limited multilingual data. While some multilingual LLMs claim to support for hundreds of languages, models often fail to provide highquality response for mid-and low-resource languages, leading to imbalanced performance heavily skewed in favor of high-resource languages like English and Chinese. We prioritize quality over scaling number of languages, with a focus on multilingual machine translation task, and introduce X-ALMA, a model designed with to ensuring top-tier performance across 50 diverse languages, regardless of their resource levels. This is achieved by plug-and-play languagespecific module architecture to prevent language conflicts during training and a carefully designed training regimen with novel optimization methods to maximize the translation performance. After the final stage of training regimen, our proposed Adaptive-Rejection Preference Optimization (ARPO) surpasses existing preference optimization methods in translation tasks. Large language models (LLMs) such as the GPT series (Brown et al., 2020; OpenAI, 2023), Mistral (Jiang et al., 2023), LLaMA series (Touvron et al., 2023a;b; Dubey et al., 2024), Gemma series (Team et al., 2024a;b), inter alia, among others, have demonstrated impressive performance across various NLP tasks. However, the efficacy of LLMs has primarily been evaluated on English tasks, with their multilingual capabilities receiving less attention due to the models being predominantly pre-trained on English and the scarcity of multilingual data. Recently, there has been a shift towards multilingual studies in LLMs. For instance, LLaMA 3 and 3.1 (Dubey et al., 2024) expand the vocabulary from 32K to 128K and pre-train on multilingual texts; Üstün et al. (2024) have introduced Aya-101, a multilingual generative model supporting 101 languages; and BigTranslate (Yang et al., 2023) and LLaMAX (Lu et al., 2024) scale LLM-based multilingual translation models to over 100 languages. Despite the increased language support in LLMs, their performance across most languages falls short of practical application expectations, especially for mid-and low-resource languages (weakness 1). Work done during an internship at Microsoft.

We propose the on-the-fly ensembling of a machine translation model with an LLM, prompted on the same task and input. We perform experiments on 4 language pairs (both directions) with varying data amounts. We find that a slightly weaker-at-translation LLM can improve translations of a NMT model, and ensembling with an LLM can produce better translations than ensembling two stronger MT models. We combine our method with various techniques from LLM prompting, such as in context learning and translation context.