3 citations · 4 across the 6 of their papers we have counts for
6 papers
AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine
Carlo Siebenschuh, Kyle Hippe, Ozan Gokdemir +10
Language models for scientific tasks are trained on text from scientific publications, most distributed as PDFs that require parsing. PDF parsing approaches range from inexpensive…
LSHBloom: Memory-efficient, Extreme-scale Document Deduplication
Arham Khan, Robert Underwood, Carlo Siebenschuh +7
Contemporary large language model (LLM) training pipelines require the assembly of internet-scale databases full of text data from a variety of sources (e.g., web, academic, and pu…
Deep Model Merging: The Sister of Neural Network Interpretability -- A Survey
Arham Khan, Todd Nief, Nathaniel Hudson +6
We survey the model merging literature through the lens of loss landscape geometry to connect observations from empirical studies on model merging and loss landscape analysis to ph…
Mitigating Memorization In Language Models
Mansi Sakarvadia, Aswathy Ajith, Arham Khan +6
Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that d…
Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism
Mansi Sakarvadia, Arham Khan, Aswathy Ajith +5
Transformer-based Large Language Models (LLMs) are the state-of-the-art for natural language tasks. Recent work has attempted to decode, by reverse engineering the role of linear l…
Memory Injections: Correcting Multi-Hop Reasoning Failures during Inference in Transformer-Based Language Models
Mansi Sakarvadia, Aswathy Ajith, Arham Khan +5
Answering multi-hop reasoning questions requires retrieving and synthesizing information from diverse sources. Large Language Models (LLMs) struggle to perform such reasoning consi…