arXiv subjects

Filippo Galgani

Publications and source records attributed to Filippo Galgani.

3 recordsLinked to original sources

arXiv2026

Gemma 4 Technical Report

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parameters. Alongside improved vision and audio encoders for all model sizes, we propose a unified, encoder-free architecture for our 12B model, which ingests raw audio and image patches. Furthermore, we integrate a thinking mode, enabling Gemma models to generate reasoning traces prior to responding. We improve inference speed, memory, and compute efficiency, as well as long-context abilities through critical design choices. Gemma 4 establishes a leap in performance across STEM, multimodal, and long-context benchmarks, and rivals larger, frontier open models in human-rated tasks.

cs.CL↗

arXiv2025

Agent-Initiated Interaction in Phone UI Automation

Phone automation agents aim to autonomously perform a given natural-language user request, such as scheduling appointments or booking a hotel. While much research effort has been devoted to screen understanding and action planning, complex tasks often necessitate user interaction for successful completion. Aligning the agent with the user's expectations is crucial for building trust and enabling personalized experiences. This requires the agent to proactively engage the user when necessary, avoiding actions that violate their preferences while refraining from unnecessary questions where a default action is expected. We argue that such subtle agent-initiated interaction with the user deserves focused research attention. To promote such research, this paper introduces a task formulation for detecting the need for user interaction and generating appropriate messages. We thoroughly define the task, including aspects like interaction timing and the scope of the agent's autonomy. Using this definition, we derived annotation guidelines and created AndroidInteraction, a diverse dataset for the task, leveraging an existing UI automation dataset. We tested several text-based and multimodal baseline models for the task, finding that it is very challenging for current LLMs. We suggest that our task formulation, dataset, baseline models and analysis will be valuable for future UI automation research, specifically in addressing this crucial yet often overlooked aspect of agent-initiated interaction. This work provides a needed foundation to allow personalized agents to properly engage the user when needed, within the context of phone UI automation.

cs.HC↗

arXiv2024

Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts

Automating data generation with Large Language Models (LLMs) has become increasingly popular. In this work, we investigate the feasibility and effectiveness of LLM-based data generation in the challenging setting of source-grounded information-seeking dialogs, with response attribution, over long documents. Our source texts consist of long and noisy meeting transcripts, adding to the task complexity. Since automating attribution remains difficult, we propose a semi-automatic approach: dialog queries and responses are generated with LLMs, followed by human verification and identification of attribution spans. Using this approach, we created MISeD -- Meeting Information Seeking Dialogs dataset -- a dataset of information-seeking dialogs focused on meeting transcripts. Models finetuned with MISeD demonstrate superior performance compared to off-the-shelf models, even those of larger size. Finetuning on MISeD gives comparable response generation quality to finetuning on fully manual data, while improving attribution quality and reducing time and effort.

cs.CL↗