Papers by Salvatore Giorgi
Building Knowledge-Guided Lexica to Model Cultural Variation (2024.naacl-long)
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| Challenge: | Cultural variation exists between nations, but also within regions . Historically, it has been difficult to computationally model cultural variation due to a lack of training data and scalability constraints. |
| Approach: | They propose a method to measure cultural variation using a knowledge-guided lexical model using geolocated tweets. |
| Outcome: | The proposed method could help us better understand the way people communicate and build more culturally-aware NLP systems. |
The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions (D18-1)
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Salvatore Giorgi, Daniel Preoţiuc-Pietro, Anneke Buffone, Daniel Rieman, Lyle Ungar, H. Andrew Schwartz
| Challenge: | Social media data is often aggregated without regard to users in the Twitter populations of each community. |
| Approach: | They propose to use Twitter language to build community-level models using Twitter language aggregated by users. |
| Outcome: | The proposed method improves on four county-level tasks spanning demographic, health, and psychological outcomes over the standard approach of aggregating all tweets. |
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas (2024.findings-emnlp)
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Salvatore Giorgi, Tingting Liu, Ankit Aich, Kelsey Isman, Garrick Sherman, Zachary Fried, João Sedoc, Lyle Ungar, Brenda Curtis
| Challenge: | Large language models (LLMs) are increasingly being used in human-centered social scientific tasks, such as data annotation, synthetic data creation, and engaging in dialog. |
| Approach: | They propose to prompt LLMs with human-like personas and ask them to answer as if they were a specific human, either explicitly, with exact demographics, political beliefs, and lived experiences, or implicitly via names prevalent in specific populations. |
| Outcome: | The proposed model is based on explicit, explicit, and implicit personas, and fails to show implicit biases. |
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks (2024.eacl-short)
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Gourab Dey, Adithya V Ganesan, Yash Kumar Lal, Manal Shah, Shreyashee Sinha, Matthew Matero, Salvatore Giorgi, Vivek Kulkarni, H. Schwartz
| Challenge: | Social science NLP tasks require large data to capture semantics and implicit pragmatics. |
| Approach: | They propose an open-source instruction tuning tool for social science NLP tasks that captures implicit pragmatic cues from text. |
| Outcome: | The proposed model matches or improves on a state-of-the-art, multi-task finetuned model on 80% of social tasks. |
Characterizing Social Spambots by their Human Traits (2021.findings-acl)
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| Challenge: | Social spambots are an emerging class of spammers attempting to emulate people . previous studies show that standard spambot detection methods fail to distinguish them from genuine accounts . |
| Approach: | They hypothesize that human-like attributes of social spambots are unhuman-like . they find that social spam bots are extremely similar and average in their expressed personality, demographics, and emotion . |
| Outcome: | The proposed method is based on the human characteristics of social spambots . it shows that social bots are extremely similar and average in their expressed personality, demographics, and emotion . |
Knowing When Not to Answer: Lightweight KB-Aligned OOD Detection for Safe RAG (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems are increasingly deployed in high-stakes domains where safety depends on how a system answers . out-of-domain (OOD) queries can impair performance and safety . |
| Approach: | They propose to use lightweight, KB-aligned OOD detection as an always-on gate for RAG systems. |
| Outcome: | The proposed method scores queries in a compact subspace selected either by explained-variance retention (EVR) or by a separability-driven -test ranking. |
Residualized Factor Adaptation for Community Social Media Prediction Tasks (D18-1)
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| Challenge: | Existing approaches to social media language capture only socio-demographic contexts, such as age, education rates, race, and gender. |
| Approach: | They propose a method which integrates community attributes and adapts linguistic features to community attributes. |
| Outcome: | The proposed model integrates community attributes and adapts linguistic features to community attributes. |
From Text to Context: Contextualizing Language with Humans, Groups, and Communities for Socially Aware NLP (2024.naacl-tutorials)
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Adithya V Ganesan, Siddharth Mangalik, Vasudha Varadarajan, Nikita Soni, Swanie Juhng, João Sedoc, H. Andrew Schwartz, Salvatore Giorgi, Ryan L Boyd
| Challenge: | This tutorial will cover the latest techniques and libraries for doing so at each level of analysis. |
| Approach: | This tutorial will cover the latest techniques and libraries for doing so at each level of analysis. |
| Outcome: | The tutorial covers human-centered techniques that provide benefit to traditional document- or word-level NLP tasks. |