Tianyu Li

Hallucination Detection and Hallucination Mitigation: An Investigation

Junliang Luo

Di Wu

M. Jenkin

Steve Liu

Gregory Dudek

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of d… (see more)ifferent applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.

2024-01-16

ArXiv (preprint)

Hallucination Detection and Hallucination Mitigation: An Investigation

Junliang Luo

Di Wu

M. Jenkin

Steve Liu

Gregory Dudek

2024-01-16

ArXiv (preprint)

Hallucination Detection and Hallucination Mitigation: An Investigation

Junliang Luo

Di Wu

M. Jenkin

Steve Liu

Gregory Dudek

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of d… (see more)ifferent applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.

2024-01-16

ArXiv (preprint)

Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning

Doina Precup

Guillaume Rabusseau

2024-01-01

Mach. Learn. (published)

Recurrent Real-valued Neural Autoregressive Density Estimator for Online Density Estimation and Classification of Streaming Data

Bogdan Mazoure

Guillaume Rabusseau

In contrast with the traditional offline learning, where complete data accessibility is assumed, many modern applications involve processing… (see more) data in a streaming fashion. This online learning setting raises various challenges, including concept drift, hardware memory constraints, etc. In this paper, we propose the Recurrent Real-valued Neural Autoregressive Density Estimator (RRNADE), a flexible density-based model for online classification and density estimation. RRNADE combines a neural Gaussian mixture density module with a recurrent module. This combination allows RRNADE to exploit possible sequential correlations in the streaming task, which are often ignored in the classical streaming setting where each input is assumed to be independent from the previous ones. We showcase the ability of RRNADE to adapt to concept drifts on synthetic density estimation tasks. We also apply RRNADE to online classification tasks on both real world and synthetic datasets and compare it with multiple density based as well as nondensity based online classification methods. In almost all of these tasks, RRNADE outperforms the other methods. Lastly, we conduct an ablation study demonstrating the complementary benefits of the density and the recurrent modules.

2023-02-01

ICLR.cc/2023/Conference (rejected)

openreview.net

Low-Rank Representation of Reinforcement Learning Policies

Thang Doan

We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional… (see more) embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability and convergence guarantees. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly represented in a low-dimensional space while the embedded policy incurs almost no decrease in returns.

2022-10-27

Journal of Artificial Intelligence Research (published)

Sequential Density Estimation via NCWFAs Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata

Bogdan Mazoure

Guillaume Rabusseau

Weighted finite automata (WFAs) have been widely applied in many fields. One of the classic problems for WFAs is probability distribution es… (see more)timation over sequences of discrete symbols. Although WFAs have been extended to deal with continuous input data, namely continuous WFAs (CWFAs), it is still unclear how to approximate density functions over sequences of continuous random variables using WFA-based models, due to the limitation on the expressiveness of the model as well as the tractability of approximating density functions via CWFAs. In this paper, we propose a nonlinear extension to the CWFA model to first improve its expressiveness, we refer to it as the nonlinear continuous WFAs (NCWFAs). Then we leverage the so-called RNADE method, which is a well-known density estimator based on neural networks, and propose the RNADE-NCWFA model. The RNADE-NCWFA model computes a density function by design. We show that this model is strictly more expressive than the Gaussian HMM model, which CWFA cannot approximate. Empirically, we conduct a synthetic experiment using Gaussian HMM generated data. We focus on evaluating the model's ability to estimate densities for sequences of varying lengths (longer length than the training data). We observe that our model performs the best among the compared baseline methods.

2022-06-08

ArXiv (preprint)

Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning

2020-06-03

Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

Provably efficient reconstruction of policy networks

Thang Doan

Recent research has shown that learning poli-cies parametrized by large neural networks can achieve significant success on challenging reinf… (see more)orcement learning problems. However, when memory is limited, it is not always possible to store such models exactly for inference, and com-pressing the policy into a compact representation might be necessary. We propose a general framework for policy representation, which reduces this problem to finding a low-dimensional embedding of a given density function in a separable inner product space. Our framework allows us to de-rive strong theoretical guarantees, controlling the error of the reconstructed policies. Such guaran-tees are typically lacking in black-box models, but are very desirable in risk-sensitive tasks. Our experimental results suggest that the reconstructed policies can use less than 10%of the number of parameters in the original networks, while incurring almost no decrease in rewards.

2020-02-07

ArXiv (preprint)

Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces.

Thang Doan

We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional… (see more) embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly embedded in a low-dimensional space while the embedded policy incurs almost no decrease in return.

Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning

Learning and planning in partially-observable domains is one of the most difficult problems in reinforcement learning. Traditional methods c… (see more)onsider these two problems as independent, resulting in a classical two-stage paradigm: first learn the environment dynamics and then plan accordingly. This approach, however, disconnects the two problems and can consequently lead to algorithms that are sample inefficient and time consuming. In this paper, we propose a novel algorithm that combines learning and planning together. Our algorithm is closely related to the spectral learning algorithm for predicitive state representations and offers appealing theoretical guarantees and time complexity. We empirically show on two domains that our approach is more sample and time efficient compared to classical methods.

2019-11-01

ArXiv (preprint)