We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family. Our work addresses the gap in linguistic technology for underrepresented languages by collecting and processing data to build a speech synthesis system. Official Codebase for "MunTTS: A…
Large language models (LLMs) have rapidly improved text embeddings for a growing array of natural-language processing tasks. However, their opaqueness and proliferation into scientific domains such as neuroscience have created a growing need for interpretability. Here, we ask whether we can obtain interpretable embeddings through…
We present Causal Amortized Active Structure Learning (CAASL), an active intervention design policy that can select interventions that are adaptive, real-time and that does not require access to the likelihood. This policy, an amortized network based on the transformer, is trained with reinforcement learning on…
TE-CCL is a tool to generate collective communication schedules for large topologies using a Traffic Engineering-based solver. TE-CCL takes in a topology and collective (e.g. AllGather) and outputs a schedule (in JSON) detailing data transfer steps for each node that satisfies the demands specified by…
A platform that enables users to perform private benchmarking of machine learning models. The platform facilitates the evaluation of models based on different trust levels between the model owners and the dataset owners.
In this post, I share learnings from my conversation with Chi Wang, a principal researcher at Microsoft and the creator of AutoGen. Agents have been a cornerstone of human-computer interaction for decades, from the friendly Clippy of Microsoft Office fame to auto-suggestions in Google Docs…
Agents have been a cornerstone of human-computer interaction for decades, from the friendly Clippy of Microsoft Office fame to auto-suggestions in Google Docs and NPCs in video games. While these early agents hinted at the potential for personalized, goal-oriented interactions, they were limited in their…
Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code. However, to fully realize their potential, we must understand how programmers interact with these systems and identify ways to improve that interaction. To make progress, we…
Social scientist and HCI expert Abigail Sellen explores the critical understanding needed to build human-centric AI through the lens of the new AICE initiative, a collective of interdisciplinary researchers studying AI impact on human cognition and the economy.
Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial training has proven to be one of the most promising methods to reliably improve robustness against such attacks. Yet, in the context of LLMs, current methods…
The integration of large language models (LLMs) into mental healthcare and research heralds a potentially transformative shift, one offering enhanced access to care, efficient data collection, and innovative therapeutic tools. This paper reviews the development, function, and burgeoning use of LLMs in psychiatry, highlighting their…
Cultural bias is pervasive in many large language models (LLMs), largely due to the deficiency of data representative of different cultures. Typically, cultural datasets and benchmarks are constructed either by extracting subsets of existing datasets or by aggregating from platforms such as Wikipedia and social…
