1 citations · 1 across the 6 of their papers we have counts for
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Cmprsr: Abstractive Token-Level Question-Agnostic Prompt Compressor
Ivan Zakazov, Berke Argin, Oussama Gabouj +6
Motivated by the high costs of using black-box Large Language Models (LLMs), we introduce a novel prompt compression paradigm, under which we use smaller LLMs to compress inputs fo…
GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning
Oussama Gabouj, Kamel Charaf, Ivan Zakazov +2
Large Language Models (LLMs) achieve strong performance across diverse tasks, but their effectiveness often depends on the quality of the provided context. Retrieval-Augmented Gene…
TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards
Andreea Nica, Ivan Zakazov, Nicolas Mario Baldwin +2
Prompt optimization improves the reasoning abilities of large language models (LLMs) without requiring parameter updates to the target model. Following heuristic-based "Think step…
RL for Reasoning by Adaptively Revealing Rationales
Mohammad Hossein Amani, Aryo Lotfi, Nicolas Mario Baldwin +4
Learning in the combinatorially large output space of sequence generation problems is challenging as providing expert demonstrations scales poorly with sequence length, and RL stru…