52 citations · 153 across the 67 of their papers we have counts for
12 papers · 1 filter
Too Big to Fool: Resisting Deception in Language Models
Mohammad Reza Samsami, Mats Leon Richter, Juan Rodriguez +3
Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. This paper investigates this interplay by…
Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs
Megh Thakkar, Quentin Fournier, Matthew Riemer +4
There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these…
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil +4
Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However, like any other software system, DRL-ba…
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…
Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models
Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh +1
Despite their widespread adoption, large language models (LLMs) remain prohibitive to use under resource constraints, with their ever growing sizes only increasing the barrier for…
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Artem Zholus, Maksim Kuznetsov, Roman Schutski +4
Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding of the complex physical interactions bet…
Sub-goal Distillation: A Method to Improve Small Language Agents
Maryam Hashemzadeh, Elias Stengel-Eskin, Sarath Chandar +1
While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational requirements and restricted number of calls…
Intelligent Switching for Reset-Free RL
Darshan Patil, Janarthanan Rajendran, Glen Berseth +1
In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resetting} assumption limits the potential of…
Manifold Metric: A Loss Landscape Approach for Predicting Model Performance
Pranshu Malviya, Jerry Huang, Aristide Baratin +2
Determining the optimal model for a given task often requires training multiple models from scratch, which becomes impractical as dataset and model sizes grow. A more efficient alt…
Interpretability Needs a New Paradigm
Andreas Madsen, Himabindu Lakkaraju, Siva Reddy +1
Interpretability is the study of explaining models in understandable terms to humans. At present, interpretability is divided into two paradigms: the intrinsic paradigm, which beli…
Towards Practical Tool Usage for Continually Learning LLMs
Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh +1
Large language models (LLMs) show an innate skill for solving language based tasks. But insights have suggested an inability to adjust for information or task-solving skills becomi…
Are self-explanations from Large Language Models faithful?
Andreas Madsen, Sarath Chandar, Siva Reddy
Instruction-tuned Large Language Models (LLMs) excel at many tasks and will even explain their reasoning, so-called self-explanations. However, convincing and wrong self-explanatio…