Search Results for author:
Found 23 papers, 10 papers with code
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training
Generalising to unseen domains is under-explored and remains a challenge in neural machine translation.
CONGRAD:Conflicting Gradient Filtering for Multilingual Preference Alignment
To address this issue, we propose CONGRAD, a scalable and effective filtering method that selects high-quality preference samples with minimal gradient conflicts across languages.
SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data to Accelerate Your Speech-LLM Development
High-quality speech dialogue datasets are crucial for Speech-LLM development, yet existing acquisition methods face significant limitations.
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model
Despite achieving remarkable performance, machine translation (MT) research remains underexplored in terms of translating cultural elements in languages, such as idioms, proverbs, and colloquial expressions.
Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation
Large language models (LLMs) have made great progress in classification and text generation tasks.
The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph
In this paper, we introduce GraphFilter, a novel method that represents the dataset as a bipartite graph, linking sentences to their constituent n-grams.
PromptDSI: Prompt-based Rehearsal-free Instance-wise Incremental Learning for Document Retrieval
Differentiable Search Index (DSI) utilizes Pre-trained Language Models (PLMs) for efficient document retrieval without relying on external indexes.
SCAR: Efficient Instruction-Tuning for Large Language Models via Style Consistency-Aware Response Ranking
Recent studies emphasize that manually ensuring a consistent response style and maintaining high data quality in training sets can significantly improve the performance of fine-tuned Large Language Models (LLMs) while reducing the number of training examples needed.
Exploring the Potential of Multimodal LLM with Knowledge-Intensive Multimodal ASR
Recent advancements in multimodal large language models (MLLMs) have made significant progress in integrating information across various modalities, yet real-world applications in educational and scientific domains remain challenging.
Mixture-of-Skills: Learning to Optimize Data Usage for Fine-Tuning Large Language Models
In this work, we propose a general, model-agnostic, reinforcement learning framework, Mixture-of-Skills (MoS), that learns to optimize data usage automatically during the fine-tuning process.
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers.
Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models
Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency.
Continual Learning for Large Language Models: A Survey
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale.
Adapting Large Language Models for Document-Level Machine Translation
We provide an in-depth analysis of these LLMs tailored for DocMT, examining translation errors, discourse phenomena, strategies for training and inference, the data efficiency of parallel documents, recent test set evaluations, and zero-shot crosslingual transfer.
Systematic Assessment of Factual Knowledge in Large Language Models
We systematically evaluate the state-of-the-art LLMs with KGs in generic and specific domains.
Simultaneous Machine Translation with Large Language Models
The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
Active Continual Learning: On Balancing Knowledge Retention and Learnability
Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL).
Koala: An Index for Quantifying Overlaps with Pre-training Corpora
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour.
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT).
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection
Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks.
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest.
Learning How to Active Learn by Dreaming
Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach
Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output.
