4 citations · 14 across the 19 of their papers we have counts for
19 papers
WorldBench: Culturally Grounded Benchmark for Multilingual Agents
Leonardo Ranaldi, Sherrie Shen, Jushi Kai +1
Despite the growing use of LLM-powered agents to solve multi-step tasks in complex environments, existing benchmarks rarely test state preservation, performance across languages, a…
Liaozhai through the Looking-Glass: On Paratextual Explicitation of Culture-Bound Terms in Machine Translation
Sherrie Shen, Weixuan Wang, Alexandra Birch
The faithful transfer of contextually-embedded meaning continues to challenge contemporary machine translation (MT), particularly in the rendering of culture-bound terms--expressio…
MGen: Millions of Naturally Occurring Generics in Context
Gustavo Cilleruelo, Emily Allaway, Barry Haddow +1
MGen is a dataset of over 4 million naturally occurring generic and quantified sentences extracted from diverse textual sources. Sentences in the dataset have long context document…
Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization
Weixuan Wang, Minghao Wu, Barry Haddow +1
Long document summarization remains a significant challenge for current large language models (LLMs), as existing approaches commonly struggle with information loss, factual incons…
The Prosody of Emojis
Giulio Zhou, Tsz Kin Lam, Alexandra Birch +1
Prosodic features such as pitch, timing, and intonation are central to spoken communication, conveying emotion, intent, and discourse structure. In text-based settings, where these…
ExpertSteer: Intervening in LLMs through Expert Knowledge
Weixuan Wang, Minghao Wu, Barry Haddow +1
Large Language Models (LLMs) exhibit remarkable capabilities across various tasks, yet guiding them to follow desired behaviours during inference remains a significant challenge. A…
HBO: Hierarchical Balancing Optimization for Fine-Tuning Large Language Models
Weixuan Wang, Minghao Wu, Barry Haddow +1
Fine-tuning large language models (LLMs) on a mixture of diverse datasets poses challenges due to data imbalance and heterogeneity. Existing methods often address these issues acro…
Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations
Leonardo Ranaldi, Federico Ranaldi, Fabio Massimo Zanzotto +2
Retrieval-augmented generation (RAG) is key to enhancing large language models (LLMs) to systematically access richer factual knowledge. Yet, using RAG brings intrinsic challenges,…
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task
Leonardo Ranaldi, Barry Haddow, Alexandra Birch
Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP, enhancing large language models (LLMs) by allowing them to access richer factual contexts through…
Demystifying Multilingual Chain-of-Thought in Process Reward Modeling
Weixuan Wang, Minghao Wu, Barry Haddow +1
Large language models (LLMs) are designed to perform a wide range of tasks. To improve their ability to solve complex problems requiring multi-step reasoning, recent research lever…
Generics are puzzling. Can language models find the missing piece?
Gustavo Cilleruelo Calderón, Emily Allaway, Barry Haddow +1
Generic sentences express generalisations about the world without explicit quantification. Although generics are central to everyday communication, building a precise semantic fram…
Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention
Weixuan Wang, Minghao Wu, Barry Haddow +1
Large Language Models (LLMs) have shown remarkable capabilities in natural language processing but exhibit significant performance gaps among different languages. Most existing app…
Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs
Weixuan Wang, Barry Haddow, Minghao Wu +2
Large language models (LLMs) have revolutionized the field of natural language processing (NLP), and recent studies have aimed to understand their underlying mechanisms. However, m…
Compact Speech Translation Models via Discrete Speech Units Pretraining
Tsz Kin Lam, Alexandra Birch, Barry Haddow
We propose a pretraining method to use Self-Supervised Speech (SSS) model to creating more compact Speech-to-text Translation. In contrast to using the SSS model for initialization…
Prosody in Cascade and Direct Speech-to-Text Translation: a case study on Korean Wh-Phrases
Giulio Zhou, Tsz Kin Lam, Alexandra Birch +1
Speech-to-Text Translation (S2TT) has typically been addressed with cascade systems, where speech recognition systems generate a transcription that is subsequently passed to a tran…
Retrieval-augmented Multilingual Knowledge Editing
Weixuan Wang, Barry Haddow, Alexandra Birch
Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time. Updating knowledge via fine-tuning is computationally resourc…
The Ups and Downs of Large Language Model Inference with Vocabulary Trimming by Language Heuristics
Nikolay Bogoychev, Pinzhen Chen, Barry Haddow +1
Deploying large language models (LLMs) encounters challenges due to intensive computational and memory requirements. Our research examines vocabulary trimming (VT) inspired by rest…
Cache & Distil: Optimising API Calls to Large Language Models
Guillem Ramírez, Matthias Lindemann, Alexandra Birch +1
Large-scale deployment of generative AI tools often depends on costly API calls to a Large Language Model (LLM) to fulfil user queries. To curtail the frequency of these calls, one…
Assessing the Reliability of Large Language Model Knowledge
Weixuan Wang, Barry Haddow, Alexandra Birch +1
Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. LLMs are typically evaluated using accuracy, yet this…