10 citations · 37 across the 29 of their papers we have counts for
28 papers · 1 filter
Do Cantonese-Adapted Language Models Better Predict Cantonese Reading? A Cross-Model Eye-Tracking Evaluation
Ziqi Zhang, Emmanuele Chersoni, Mohammad Momenian
Information-theoretic measures derived from autoregressive language models are widely used to characterize the expectations that shape human reading, but whether language-variety-s…
Know It, Act on It: Investigating Memory Utilization in LLM Personalization
Zhaoxin Feng, Jianfei Ma, Emmanuele Chersoni
As large language model (LLM) agents evolve into personalized companions, memory has emerged as a core capability. However, LLMs face a knowledge utilization problem: they may fail…
Every Time I Hire a Linguist, Inference Costs Go Down: On Linguistic Rules as Effective Prompt Compressors
Jianfei Ma, Zhaoxin Feng, Emmanuele Chersoni +1
Prompt compression shortens LLM input to reduce inference cost, yet existing methods score token importance through LM forward passes. It remains questionable whether such nuanced,…
Is Length Really A Liability? An Evaluation of Multi-turn LLM Conversations using BoolQ
Karl Neergaard, Le Qiu, Emmanuele Chersoni
Single-prompt evaluations dominate current LLM benchmarking, yet they fail to capture the conversational dynamics where real-world harm occurs. In this study, we examined whether c…
Can Large Language Models Understand, Reason About, and Generate Code-Switched Text?
Genta Indra Winata, David Anugraha, Patrick Amadeus Irawan +15
Code-switching is a pervasive phenomenon in multilingual communication, yet the robustness of large language models (LLMs) in mixed-language settings remains insufficiently underst…
Learning to Look at the Other Side: A Semantic Probing Study of Word Embeddings in LLMs with Enabled Bidirectional Attention
Zhaoxin Feng, Jianfei Ma, Emmanuele Chersoni +2
Autoregressive Large Language Models (LLMs) demonstrate exceptional performance in language understanding and generation. However, their application in text embedding tasks has bee…
From BERT to LLMs: Comparing and Understanding Chinese Classifier Prediction in Language Models
Ziqi Zhang, Jianfei Ma, Emmanuele Chersoni +2
Classifiers are an important and defining feature of the Chinese language, and their correct prediction is key to numerous educational applications. Yet, whether the most popular L…
Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs
Yiheng Yang, Yujie Wang, Chi Ma +3
Dense large language models(LLMs) face critical efficiency bottlenecks as they rigidly activate all parameters regardless of input complexity. While existing sparsity methods(stati…
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan +48
Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate th…
Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models
Carina Kauf, Emmanuele Chersoni, Alessandro Lenci +2
Semantic plausibility (e.g. knowing that "the actor won the award" is more likely than "the actor won the battle") serves as an effective proxy for general world knowledge. Languag…
Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction
Simone Scaboro, Beatrice Portellia, Emmanuele Chersoni +2
Adverse Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts. This task has been addressed by the Natural Languag…
Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures
Jakob Prange, Emmanuele Chersoni
In this work we build upon negative results from an attempt at language modeling with predicted semantic structure, in order to establish empirical lower bounds on what could have…
Event knowledge in large language models: the gap between the impossible and the unlikely
Carina Kauf, Anna A. Ivanova, Giulia Rambelli +5
Word co-occurrence patterns in language corpora contain a surprising amount of conceptual knowledge. Large language models (LLMs), trained to predict words in context, leverage the…
Generalizing over Long Tail Concepts for Medical Term Normalization
Beatrice Portelli, Simone Scaboro, Enrico Santus +3
Medical term normalization consists in mapping a piece of text to a large number of output classes. Given the small size of the annotated datasets and the extremely long tail distr…
AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles
Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni +2
This paper describes the models developed by the AILAB-Udine team for the SMM4H 22 Shared Task. We explored the limits of Transformer based models on text classification, entity ex…
Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation
Simone Scaboro, Beatrice Portelli, Emmanuele Chersoni +2
In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums. Given the large volume of re…
NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations
Simone Scaboro, Beatrice Portelli, Emmanuele Chersoni +2
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical inv…
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media
Shivam Raval, Hooman Sedghamiz, Enrico Santus +3
Adverse Events (AE) are harmful events resulting from the use of medical products. Although social media may be crucial for early AE detection, the sheer scale of this data makes i…
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge
Paolo Pedinotti, Giulia Rambelli, Emmanuele Chersoni +3
Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate. While much work has been devoted to modeling the typicality r…
Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies
Beatrice Portelli, Daniele Passabì, Edoardo Lenzi +3
In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums. Because of the large volume of reports, pharmacovigilance is seeki…
A Structured Distributional Model of Sentence Meaning and Processing
Emmanuele Chersoni, Enrico Santus, Ludovica Pannitto +3
Most compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a Structured Distributional Model (SDM) that combines w…
A Rank-Based Similarity Metric for Word Embeddings
Enrico Santus, Hongmin Wang, Emmanuele Chersoni +1
Word Embeddings have recently imposed themselves as a standard for representing word meaning in NLP. Semantic similarity between word pairs has become the most common evaluation be…
BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes
Enrico Santus, Chris Biemann, Emmanuele Chersoni
This paper describes BomJi, a supervised system for capturing discriminative attributes in word pairs (e.g. yellow as discriminative for banana over watermelon). The system relies…
Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?
Emmanuele Chersoni, Enrico Santus, Philippe Blache +1
Despite the number of NLP studies dedicated to thematic fit estimation, little attention has been paid to the related task of composing and updating verb argument expectations. The…