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Found 12 papers, 2 papers with code
Gradient-Based Language Model Red Teaming
Red teaming is a common strategy for identifying weaknesses in generative language models (LMs), where adversarial prompts are produced that trigger an LM to generate unsafe responses.
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation
We investigate this approach under two different settings: one where the policy model is smaller and is paired with a more powerful critic model, and another where a single language model fulfills both roles.
Fusion-Eval: Integrating Evaluators with LLMs
Evaluating natural language systems poses significant challenges, particularly in the realms of natural language understanding and high-level reasoning.
SiRA: Sparse Mixture of Low Rank Adaptation
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks.
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition
However, we identify these techniques are not well equipped for safe policy learning because they ignore negative experiences(e. g., unsafe or unsuccessful), focusing only on positive experiences, which harms their ability to generalize to new tasks safely.
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition
Though many reinforcement learning (RL) problems involve learning policies in settings that are difficult to specify safety constraints and sparse rewards, current methods struggle to rapidly and safely acquire successful policies.
ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces
Our methodology is designed to leverage visual, linguistic and domain-specific features in user interaction traces to pre-train generic feature representations of UIs and their components.
RL agents Implicitly Learning Human Preferences
In the real world, RL agents should be rewarded for fulfilling human preferences.
Resolving Spurious Correlations in Causal Models of Environments via Interventions
We consider the problem of inferring a causal model of a reinforcement learning environment and we propose a method to deal with spurious correlations.
Resolving Referring Expressions in Images With Labeled Elements
Images may have elements containing text and a bounding box associated with them, for example, text identified via optical character recognition on a computer screen image, or a natural image with labeled objects.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Hierarchical Long-term Video Prediction without Supervision
Much of recent research has been devoted to video prediction and generation, yet most of the previous works have demonstrated only limited success in generating videos on short-term horizons.
Unsupervised Hierarchical Video Prediction
Much recent research has been devoted to video prediction and generation, but mostly for short-scale time horizons.
