6 citations · 8 across the 6 of their papers we have counts for
6 papers
Document Optimization for Black-Box Retrieval via Reinforcement Learning
Omri Uzan, Ron Polonsky, Douwe Kiela +1
Generative large language models (LLMs) are increasingly used as inference-time components in retrieval pipelines, for tasks such as query rewriting and document reranking. However…
Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization
Omri Uzan, Asaf Yehudai, Roi pony +2
Multimodal encoders have pushed the boundaries of visual document retrieval, matching textual query tokens directly to image patches and achieving state-of-the-art performance on p…
CharBench: Evaluating the Role of Tokenization in Character-Level Tasks
Omri Uzan, Yuval Pinter
Tasks that require character-level reasoning, such as counting or locating characters within words, remain challenging for contemporary language models. A common conjecture is that…
Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge
Khuyagbaatar Batsuren, Ekaterina Vylomova, Verna Dankers +4
The popular subword tokenizers of current language models, such as Byte-Pair Encoding (BPE), are known not to respect morpheme boundaries, which affects the downstream performance…
Greed is All You Need: An Evaluation of Tokenizer Inference Methods
Omri Uzan, Craig W. Schmidt, Chris Tanner +1
While subword tokenizers such as BPE and WordPiece are typically used to build vocabularies for NLP models, the method of decoding text into a sequence of tokens from these vocabul…
Tokenization Is More Than Compression
Craig W. Schmidt, Varshini Reddy, Haoran Zhang +4
Tokenization is a foundational step in natural language processing (NLP) tasks, bridging raw text and language models. Existing tokenization approaches like Byte-Pair Encoding (BPE…