A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper introduces M-RewardBench, a benchmark for evaluating reward models in multilingual settings, revealing that current reward models exhibit limited robustness across languages.
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Proposes MRRE, a training-free inference-time method using representation engineering to enhance multilingual reasoning in LLMs and LVLMs without additional training data.
Melvin Johnson, Mike Schuster, Quoc V. Le, et al.
Proposes a single multilingual NMT model using a target-language token for zero-shot translation across multiple language pairs.
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Creates a 2.8M-pair multilingual parallel corpus for scientific/technical MT in eight low-resource Indian languages from NPTEL lecture transcriptions.
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Leverages Gemini LLM to generate generalizable multilingual text and code embeddings via synthetic data and filtering.
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SigLIP 2 improves multilingual vision-language encoders with captioning pretraining, self-supervised losses, and online data curation, offering native aspect ratio preservation.
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mmE5 is a multimodal multilingual embedding model trained on synthetic data via a framework that ensures broad task/language coverage, robust cross-modal alignment, and high fidelity through self-evaluation and refinement.
Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, et al.
Jina Embeddings v3 is a 570M parameter multilingual embedding model supporting 8192-token contexts, task-specific LoRA adapters, and flexible dimension reduction via Matryoshka learning.
Saurabh Dash, Yiyang Nan, John Dang, et al.
Aya Vision introduces open-weight multilingual VLMs for 23 languages using synthetic annotation and cross-modal model merging to retain text skills.
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Maya is an open-source multilingual multimodal model for vision-language understanding in eight languages, built on a toxicity-filtered dataset and fine-tuned on PALO 150K.
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PaLI-X scales vision-language models to 55B parameters, achieving SOTA on 25+ benchmarks via emergent abilities like counting and multilingual detection.
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PaLI is a joint language-vision model that generates multilingual text from visual and textual inputs using large pre-trained encoder-decoder language models and Vision Transformers.