8 citations · 10 across the 10 of their papers we have counts for
11 papers
Latent-Space Intervention for Cross-Lingual Factual Consistency: Consistency Improvements without Accuracy Drops
Faeze Ghorbanpour, Constanza Fierro, Alexander Fraser +1
Large Language Models (LLMs) often answer the same factual question differently across languages. We study whether cross-lingual latent-space intervention can reduce this inconsist…
EvalCards: A Framework for Standardized Evaluation Reporting
Ruchira Dhar, Danae Sanchez Villegas, Antonia Karamolegkou +11
Evaluation reports guide the use of general-purpose AI (GPAI) models, yet inconsistent reporting often leaves essential questions unanswered. Through a review of prior work, we ide…
Trick or Neat: Adversarial Ambiguity and Language Model Evaluation
Antonia Karamolegkou, Oliver Eberle, Phillip Rust +2
Detecting ambiguity is important for language understanding, including uncertainty estimation, humour detection, and processing garden path sentences. We assess language models' se…
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment
Antonia Karamolegkou, Angana Borah, Eunjung Cho +30
Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social G…
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users
Antonia Karamolegkou, Malvina Nikandrou, Georgios Pantazopoulos +5
This paper explores the effectiveness of Multimodal Large Language models (MLLMs) as assistive technologies for visually impaired individuals. We conduct a user survey to identify…
Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements
Antonia Karamolegkou, Sandrine Schiller Hansen, Ariadni Christopoulou +3
What ethical concerns, if any, do LLM researchers have? We introduce EthiCon, a corpus of 1,580 ethical concern statements extracted from scientific papers published in the ACL Ant…
How Do Multilingual Language Models Remember Facts?
Constanza Fierro, Negar Foroutan, Desmond Elliott +1
Large Language Models (LLMs) store and retrieve vast amounts of factual knowledge acquired during pre-training. Prior research has localized and identified mechanisms behind knowle…
Vision-Language Models under Cultural and Inclusive Considerations
Antonia Karamolegkou, Phillip Rust, Yong Cao +3
Large vision-language models (VLMs) can assist visually impaired people by describing images from their daily lives. Current evaluation datasets may not reflect diverse cultural us…
MuLan: A Study of Fact Mutability in Language Models
Constanza Fierro, Nicolas Garneau, Emanuele Bugliarello +2
Facts are subject to contingencies and can be true or false in different circumstances. One such contingency is time, wherein some facts mutate over a given period, e.g., the presi…
Does Instruction Tuning Make LLMs More Consistent?
Constanza Fierro, Jiaang Li, Anders Søgaard
The purpose of instruction tuning is enabling zero-shot performance, but instruction tuning has also been shown to improve chain-of-thought reasoning and value alignment (Si et al.…
Copyright Violations and Large Language Models
Antonia Karamolegkou, Jiaang Li, Li Zhou +1
Language models may memorize more than just facts, including entire chunks of texts seen during training. Fair use exemptions to copyright laws typically allow for limited use of c…