1 citations · 1 across the 6 of their papers we have counts for
8 papers
Cross-Modal Taxonomic Generalization in (Vision-) Language Models
Tianyang Xu, Marcelo Sandoval-Castaneda, Karen Livescu +2
What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grounded evidence? We study this question…
OKBench: Democratizing LLM Evaluation with Fully Automated, On-Demand, Open Knowledge Benchmarking
Yanhong Li, Tianyang Xu, Kenan Tang +3
Knowledge-intensive question answering is central to large language models (LLMs) and is typically assessed using static benchmarks derived from sources like Wikipedia and textbook…
Transcribe, Translate, or Transliterate: An Investigation of Intermediate Representations in Spoken Language Models
Tolúlopé Ògúnrèmí, Christopher D. Manning, Dan Jurafsky +1
Spoken language models (SLMs) that integrate speech with large language models (LMs) rely on modality adapters (MAs) to map the output of speech encoders to a representation that i…
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties
William Chen, Chutong Meng, Jiatong Shi +10
Recent improvements in multilingual ASR have not been equally distributed across languages and language varieties. To advance state-of-the-art (SOTA) ASR models, we present the Int…
Flow-SLM: Joint Learning of Linguistic and Acoustic Information for Spoken Language Modeling
Ju-Chieh Chou, Jiawei Zhou, Karen Livescu
Textless spoken language models (SLMs) are generative models of speech that do not rely on text supervision. Most textless SLMs learn to predict the next semantic token, a discrete…
On the Predictive Power of Representation Dispersion in Language Models
Yanhong Li, Ming Li, Karen Livescu +1
We show that a language model's ability to predict text is tightly linked to the breadth of its embedding space: models that spread their contextual representations more widely ten…