2 citations · 4 across the 20 of their papers we have counts for
20 papers
SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers
Aref Jafari, Yuhe Fan, Benyamin Jamialahmadi +3
Transformers have demonstrated strong performance across a wide range of sequence modeling tasks, but their quadratic attention complexity limits scalability to long sequences. Lin…
DTRNet: Dynamic Token Routing Network to Reduce Quadratic Costs in Transformers
Aman Sharma, Saeed Najafi, Parsa Farinneya +6
Transformers achieve state-of-the-art results across many tasks, but their uniform application of quadratic self-attention to every token at every layer makes them computationally…
Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models
Benyamin Jamialahmadi, Parsa Kavehzadeh, Mehdi Rezagholizadeh +5
Deploying large language models (LLMs) in real-world applications is often hindered by strict computational and latency constraints. While dynamic inference offers the flexibility…
ReGLA: Refining Gated Linear Attention
Peng Lu, Ivan Kobyzev, Mehdi Rezagholizadeh +2
Recent advancements in Large Language Models (LLMs) have set themselves apart with their exceptional performance in complex language modelling tasks. However, these models are also…
ZETA: Leveraging Z-order Curves for Efficient Top-k Attention
Qiuhao Zeng, Jerry Huang, Peng Lu +4
Over recent years, the Transformer has become a fundamental building block for sequence modeling architectures. Yet at its core is the use of self-attention, whose memory and compu…
Batch-Max: Higher LLM Throughput using Larger Batch Sizes and KV Cache Compression
Michael R. Metel, Boxing Chen, Mehdi Rezagholizadeh
Several works have developed eviction policies to remove key-value (KV) pairs from the KV cache for more efficient inference. The focus has been on compressing the KV cache after t…
Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination
Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh +2
The growth in prominence of large language models (LLMs) in everyday life can be largely attributed to their generative abilities, yet some of this is also owed to the risks and co…
Draft on the Fly: Adaptive Self-Speculative Decoding using Cosine Similarity
Michael R. Metel, Peng Lu, Boxing Chen +2
We present a simple on the fly method for faster inference of large language models. Unlike other (self-)speculative decoding techniques, our method does not require fine-tuning or…
EchoAtt: Attend, Copy, then Adjust for More Efficient Large Language Models
Hossein Rajabzadeh, Aref Jafari, Aman Sharma +5
Large Language Models (LLMs), with their increasing depth and number of parameters, have demonstrated outstanding performance across a variety of natural language processing tasks.…
S2D: Sorted Speculative Decoding For More Efficient Deployment of Nested Large Language Models
Parsa Kavehzadeh, Mohammadreza Pourreza, Mojtaba Valipour +5
Deployment of autoregressive large language models (LLMs) is costly, and as these models increase in size, the associated costs will become even more considerable. Consequently, di…
EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems
Mohammad Dehghan, Mohammad Ali Alomrani, Sunyam Bagga +12
The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems i…
OTTAWA: Optimal TransporT Adaptive Word Aligner for Hallucination and Omission Translation Errors Detection
Chenyang Huang, Abbas Ghaddar, Ivan Kobyzev +3
Recently, there has been considerable attention on detecting hallucinations and omissions in Machine Translation (MT) systems. The two dominant approaches to tackle this task invol…
CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
Fengran Mo, Abbas Ghaddar, Kelong Mao +4
In this paper, we study how open-source large language models (LLMs) can be effectively deployed for improving query rewriting in conversational search, especially for ambiguous qu…
CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems
Abbas Ghaddar, David Alfonso-Hermelo, Philippe Langlais +3
In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial…
An Efficient End-to-End Approach to Noise Invariant Speech Features via Multi-Task Learning
Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila +3
Self-supervised speech representation learning enables the extraction of meaningful features from raw waveforms. These features can then be efficiently used across multiple downstr…
QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning
Hossein Rajabzadeh, Mojtaba Valipour, Tianshu Zhu +5
Finetuning large language models requires huge GPU memory, restricting the choice to acquire Larger models. While the quantized version of the Low-Rank Adaptation technique, named…
On the importance of Data Scale in Pretraining Arabic Language Models
Abbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh +1
Pretraining monolingual language models have been proven to be vital for performance in Arabic Natural Language Processing (NLP) tasks. In this paper, we conduct a comprehensive st…
"Knowing When You Don't Know": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation
Nandan Thakur, Luiz Bonifacio, Xinyu Zhang +8
Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations. However, prior work lacks…
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference
Parsa Kavehzadeh, Mojtaba Valipour, Marzieh Tahaei +3
Large language models (LLMs) have revolutionized natural language processing (NLP) by excelling at understanding and generating human-like text. However, their widespread deploymen…
SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks
Mojtaba Valipour, Mehdi Rezagholizadeh, Hossein Rajabzadeh +4
Deep neural networks (DNNs) must cater to a variety of users with different performance needs and budgets, leading to the costly practice of training, storing, and maintaining nume…