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Found 15 papers, 4 papers with code
GI-Free Pilot-Aided Channel Estimation for Affine Frequency Division Multiplexing Systems
The recently developed affine frequency division multiplexing (AFDM) can achieve full diversity in doubly selective channels, providing a comprehensive sparse representation of the delay-Doppler domain channel.
Improving Reinforcement Learning from Human Feedback Using Contrastive Rewards
Reinforcement learning from human feedback (RLHF) is the mainstream paradigm used to align large language models (LLMs) with human preferences.
Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
We introduce Adversarial Policy Optimization (AdvPO), a novel solution to the pervasive issue of reward over-optimization in Reinforcement Learning from Human Feedback (RLHF) for Large Language Models (LLMs).
Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation
Despite showing increasingly human-like abilities, large language models (LLMs) often struggle with factual inaccuracies, i. e. "hallucinations", even when they hold relevant knowledge.
Human-Instruction-Free LLM Self-Alignment with Limited Samples
The key idea is to first retrieve high-quality samples related to the target domain and use them as In-context Learning examples to generate more samples.
Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?
Our analysis exhibits the potentials and flaws in existing resources (models and datasets) in developing explainable moral judgment-making systems.
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations.
SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting
Building end-to-end task bots and maintaining their integration with new functionalities using minimal human efforts is a long-standing challenge in dialog research.
Debiasing Recommendation by Learning Identifiable Latent Confounders
Confounding bias arises due to the presence of unmeasured variables (e. g., the socio-economic status of a user) that can affect both a user's exposure and feedback.
Disentangled Representation for Diversified Recommendations
This calls for a fine-grained understanding of a user's preferences over items, where one needs to recognize the user's choice is driven by the quality of the item itself, or the pre-selected attributes of the item.
Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples
This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform.
Toward Self-learning End-to-End Task-Oriented Dialog Systems
In this paper, we study the problem of automatically adapting task bots to changing environments by learning from human-bot interactions with minimum or zero human annotations.
A Low Complexity Learning-based Channel Estimation for OFDM Systems with Online Training
Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission.
Unstructured Knowledge Access in Task-oriented Dialog Modeling using Language Inference, Knowledge Retrieval and Knowledge-Integrative Response Generation
Dialog systems enriched with external knowledge can handle user queries that are outside the scope of the supporting databases/APIs.
Conversational Contextual Bandit: Algorithm and Application
Here, a key-term can relate to a subset of arms, for example, a category of articles in news recommendation.
