2 citations · 6 across the 13 of their papers we have counts for
13 papers
Thinking into the Future: Latent Lookahead Training for Transformers
Lorenzo Noci, Gregor Bachmann, Seyed-Mohsen Moosavi-Dezfooli +1
Autoregressive language models trained with next-token prediction generate text by sampling one discrete token at a time. Although very scalable, this objective forces the model to…
The Potential of CoT for Reasoning: A Closer Look at Trace Dynamics
Gregor Bachmann, Yichen Jiang, Seyed Mohsen Moosavi Dezfooli +1
Chain-of-thought (CoT) prompting is a de-facto standard technique to elicit reasoning-like responses from large language models (LLMs), allowing them to spell out individual steps…
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
Learning Private Representations through Entropy-based Adversarial Training
Tassilo Klein, Moin Nabi
How can we learn a representation with high predictive power while preserving user privacy? We present an adversarial representation learning method for sanitizing sensitive conten…
From Dense to Dynamic: Token-Difficulty Driven MoEfication of Pre-Trained LLMs
Kumari Nishu, Sachin Mehta, Samira Abnar +6
Training large language models (LLMs) for different inference constraints is computationally expensive, limiting control over efficiency-accuracy trade-offs. Moreover, once trained…
Computational Bottlenecks of Training Small-scale Large Language Models
Saleh Ashkboos, Iman Mirzadeh, Keivan Alizadeh +4
While large language models (LLMs) dominate the AI landscape, Small-scale large Language Models (SLMs) are gaining attention due to cost and efficiency demands from consumers. Howe…
Multimodal Autoregressive Pre-training of Large Vision Encoders
Enrico Fini, Mustafa Shukor, Xiujun Li +13
We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framewo…
SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHF
Atoosa Chegini, Hamid Kazemi, Iman Mirzadeh +5
In Large Language Model (LLM) development, Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning models with human values and preferences. RLHF traditionally re…
KV Prediction for Improved Time to First Token
Maxwell Horton, Qingqing Cao, Chenfan Sun +4
Inference with transformer-based language models begins with a prompt processing step. In this step, the model generates the first output token and stores the KV cache needed for f…
Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models
Keivan Alizadeh, Iman Mirzadeh, Hooman Shahrokhi +6
Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recen…
Chain-of-Sketch: Enabling Global Visual Reasoning
Aryo Lotfi, Enrico Fini, Samy Bengio +2
Modern vision models have achieved remarkable success in benchmarks where local features provide critical information about the target. There is now a growing interest in tackling…
Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization
Mohammad Samragh, Iman Mirzadeh, Keivan Alizadeh Vahid +5
The pre-training phase of language models often begins with randomly initialized parameters. With the current trends in scaling models, training their large number of parameters ca…
Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Models
Tassilo Klein, Moin Nabi
The generation of toxic content by large language models (LLMs) remains a critical challenge for the safe deployment of language technology. We propose a novel framework for implic…