Optimizing Delegation in Collaborative Human-AI Hybrid Teams
The study presents a framework for hybrid teams comprising of humans and autonomous systems, or agents. The…
Prompting Fairness: Artificial Intelligence as Game Players
This academic paper by Jazmia Henry from the University of Oxford investigates the concept of fairness in…
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
This research paper presents R3, a novel method for training large language models (LLMs) in complex reasoning…
Limitations of Agents Simulated by Predictive Models
The article focuses on the limitations of predictive models when adapted into agent-like systems. Two structural reasons…
How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis
This research paper investigates the negotiation abilities of Large Language Models (LLMs). The authors developed a flexible…
Large Language Model Meets Graph Neural Network in Knowledge Distillation
The article discusses a new graph knowledge distillation framework, named LinguGKD, using Large Language Models (LLMs) as…
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
The study presents aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning….
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
The research paper discusses the potential of causal knowledge in improving the interpretability of reinforcement learning (RL)…
On Provable Length and Compositional Generalization
This research paper delves into length and compositional generalization in sequence-to-sequence models. These forms of out-of-distribution (OOD)…
L4Q: Parameter Efficient Quantization-Aware Training on Large Language Models via LoRA-wise LSQ
This article introduces L4Q, a novel algorithm for parameter-efficient quantization-aware training on Large Language Models (LLMs). L4Q…