483 citations · 3k across the 177 of their papers we have counts for
8 papers · 1 filter
Which LLM to pick? Online Active Model Selection for Large Language Models
Alessandro Turrin, Patrik Okanovic, Torsten Hoefler +1
Large Language Models (LLMs) are increasingly applied to process streaming data, with practitioners relying on benchmarks to select the best model even though these signals only ap…
Large Language Model Selection with Limited Annotations
Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch +2
Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotations over fixed evaluation sets. T…
Multi-Head RAG: Solving Multi-Aspect Problems with LLMs
Maciej Besta, Ales Kubicek, Robert Gerstenberger +13
Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by retrieving supporting documents into the prompt, but existing methods do not explicitly target queries…
CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks
Maciej Besta, Lorenzo Paleari, Marcin Copik +9
Large Language Models (LLMs) are transforming a wide range of domains, yet verifying their outputs remains a significant challenge, especially for complex open-ended tasks such as…
Demystifying Chains, Trees, and Graphs of Thoughts
Maciej Besta, Florim Memedi, Zhenyu Zhang +13
The field of natural language processing (NLP) has witnessed significant progress in recent years, with a notable focus on improving large language models' (LLM) performance throug…
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark
Eldar Kurtic, Torsten Hoefler, Dan Alistarh
Pruning large language models (LLMs) from the BERT family has emerged as a standard compression benchmark, and several pruning methods have been proposed for this task. The recent…
Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Maciej Besta, Nils Blach, Ales Kubicek +8
We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or…
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian +6
Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per paramet…