Ambuj K Singh

Sikun Lin, Thomas Sprague, Ambuj K Singh

Understanding how the brain encodes external stimuli and how these stimuli can be decoded from the measured brain activities are long-standing and challenging questions in neuroscience. In this paper, we focus on reconstructing the complex image stimuli from fMRI (functional magnetic resonance imaging) signals. Unlike previous works that reconstruct images with single objects or simple shapes, our work aims to reconstruct image stimuli that are rich in semantics, closer to everyday scenes, and can reveal more perspectives. However, data scarcity of fMRI datasets is the main obstacle to applying state-of-the-art deep learning models to this problem. We find that incorporating an additional text modality is beneficial for the reconstruction problem compared to directly translating brain signals to images. Therefore, the modalities involved in our method are: (i) voxel-level fMRI signals, (ii) observed images that trigger the brain signals, and (iii) textual description of the images. To further address data scarcity, we leverage an aligned vision-language latent space pre-trained on massive datasets. Instead of training models from scratch to find a latent space shared by the three modalities, we encode fMRI signals into this pre-aligned latent space. Then, conditioned on embeddings in this space, we reconstruct images with a generative model. The reconstructed images from our pipeline balance both naturalness and fidelity: they are photo-realistic and capture the ground truth image contents well.

Zichen Chen, Jianda Chen, Yuanyuan Chen et al.

Language models (LMs) like GPT-4 are important in AI applications, but their opaque decision-making process reduces user trust, especially in safety-critical areas. We introduce LMExplainer, a novel knowledge-grounded explainer that clarifies the reasoning process of LMs through intuitive, human-understandable explanations. By leveraging a graph attention network (GAT) with a large-scale knowledge graph (KG), LMExplainer not only precisely narrows the reasoning space to focus on the most relevant knowledge but also grounds its reasoning in structured, verifiable knowledge to reduce hallucinations and enhance interpretability. LMExplainer effectively generates human-understandable explanations to enhance transparency and streamline the decision-making process. Additionally, by incorporating debugging into the explanation, it offers expertise suggestions that improve LMs from a developmental perspective. Thus, LMExplainer stands as an enhancement in making LMs more accessible and understandable to users. We evaluate LMExplainer on benchmark datasets such as CommonsenseQA and OpenBookQA, demonstrating that it outperforms most existing methods. By comparing the explanations generated by LMExplainer with those of other models, we show that our approach offers more comprehensive and clearer explanations of the reasoning process. LMExplainer provides a deeper understanding of the inner workings of LMs, advancing towards more reliable, transparent, and equitable AI.

Wei Ye, Francesco Bullo, Noah Friedkin et al.

AI and humans bring complementary skills to group deliberations. Modeling this group decision making is especially challenging when the deliberations include an element of risk and an exploration-exploitation process of appraising the capabilities of the human and AI agents. To investigate this question, we presented a sequence of intellective issues to a set of human groups aided by imperfect AI agents. A group's goal was to appraise the relative expertise of the group's members and its available AI agents, evaluate the risks associated with different actions, and maximize the overall reward by reaching consensus. We propose and empirically validate models of human-AI team decision making under such uncertain circumstances, and show the value of socio-cognitive constructs of prospect theory, influence dynamics, and Bayesian learning in predicting the behavior of human-AI groups.