19 citations · 78 across the 27 of their papers we have counts for
10 papers · 1 filter
AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution
Fengyuan Liu, Nikhil Kandpal, Colin Raffel
The influence of contextual input on the behavior of large language models (LLMs) has prompted the development of context attribution methods that aim to quantify each context span…
Realistic Evaluation of Model Merging for Compositional Generalization
Derek Tam, Yash Kant, Brian Lester +2
Merging has become a widespread way to cheaply combine individual models into a single model that inherits their capabilities and attains better performance. This popularity has sp…
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
Prateek Yadav, Colin Raffel, Mohammed Muqeeth +6
The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particular domain or task. Model MoErging methods…
The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal +5
The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs…
Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Bowen Pan, Yikang Shen, Haokun Liu +5
Mixture-of-Experts (MoE) language models can reduce computational costs by 2-4 compared to dense models without sacrificing performance, making them more efficient in compu…
"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
Vipula Rawte, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +4
Hallucination has emerged as the most vulnerable aspect of contemporary Large Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA) prompting, aimed to av…
A Survey on Data Selection for Language Models
Alon Albalak, Yanai Elazar, Sang Michael Xie +11
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model…
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Ajay Patel, Colin Raffel, Chris Callison-Burch
Large language models (LLMs) have become a dominant and important tool for NLP researchers in a wide range of tasks. Today, many researchers use LLMs in synthetic data generation,…
Learning to Route Among Specialized Experts for Zero-Shot Generalization
Mohammed Muqeeth, Haokun Liu, Yufan Liu +1
Recently, there has been a widespread proliferation of "expert" language models that are specialized to a specific task or domain through parameter-efficient fine-tuning. How can w…
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9
Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…