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20222025
most citedScaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

27 citations · 45 across the 12 of their papers we have counts for

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6 papers · 1 filter

cs.CL2025

Adaptive Originality Filtering: Rejection Based Prompting and RiddleScore for Culturally Grounded Multilingual Riddle Generation

Duy Le, Kent Ziti, Evan Girard-Sun +4

Language models are increasingly tested on multilingual creativity, demanding culturally grounded, abstract generations. Standard prompting methods often produce repetitive or shal…

cs.CL2025

Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT

Timothy Do, Pranav Saran, Harshita Poojary +4

In this paper, we address the persistent challenges that figurative language expressions pose for natural language processing (NLP) systems, particularly in low-resource languages…

cs.CL2025

NovelHopQA: Diagnosing Multi-Hop Reasoning Failures in Long Narrative Contexts

Abhay Gupta, Michael Lu, Kevin Zhu +2

Current large language models (LLMs) struggle to answer questions that span tens of thousands of tokens, especially when multi-hop reasoning is involved. While prior benchmarks exp…

cs.CL2025

Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representations

Abhilekh Borah, Chhavi Sharma, Danush Khanna +12

Alignment is no longer a luxury, it is a necessity. As large language models (LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must r…

cs.CL2025

Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language

Jesus Alvarez C, Daua D. Karajeanes, Ashley Celeste Prado +5

The digital exclusion of endangered languages remains a critical challenge in NLP, limiting both linguistic research and revitalization efforts. This study introduces the first com…

cs.CL20245 cited

Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

Sainbayar Sukhbaatar, Olga Golovneva, Vasu Sharma +8

We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowled…