15 papers
Massively Multilingual Joint Segmentation and Glossing
Michael Ginn, Lindia Tjuatja, Enora Rice +5
Automated interlinear gloss prediction with neural networks is a promising approach to accelerate language documentation efforts. However, while state-of-the-art models like GlossL…
What do Language Models Learn and When? The Implicit Curriculum Hypothesis
Emmy Liu, Kaiser Sun, Millicent Li +4
Large language models (LLMs) can perform remarkably complex tasks, yet the fine-grained details of how these capabilities emerge during pretraining remain poorly understood. Scalin…
IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation
Anjali Kantharuban, Aarohi Srivastava, Fahim Faisal +5
Existing sentence representations primarily encode what a sentence says, rather than how it is expressed, even though the latter is important for many applications. In contrast, we…
ZINA: Multimodal Fine-grained Hallucination Detection and Editing
Yuiga Wada, Kazuki Matsuda, Komei Sugiura +1
Multimodal Large Language Models (MLLMs) often generate hallucinations, where the output deviates from the visual content. Given that these hallucinations can take diverse forms, d…
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
Steven Kolawole, Lucio Dery, Jean-François Kagy +3
Structured pruning is a promising approach to create smaller, faster large language models. However, existing methods typically rely on computing the gradient via backward passes,…
ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation
Yiming Xu, Yuan Yuan, Vijay Viswanathan +1
Text clustering is a fundamental task in natural language processing, yet traditional clustering algorithms with pre-trained embeddings often struggle in domain-specific contexts w…