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20242026
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cs.CL2026

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning

Ajay Patel, Kartik Hosanagar, Ramayya Krishnan +3

Large language models (LLMs) are improving rapidly as reflected in benchmark scores, yet these AI benchmarks largely test capabilities such as factual recall, narrow question answe…

cs.CL2026

FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale

Ajay Patel, Colin Raffel, Chris Callison-Burch

Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised "predict the next word" objective on a vast amount of unstruct…

cs.CL2026

ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders

Ofer Meshi, Krisztian Balog, Sally Goldman +5

The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…

cs.CL2025

mStyleDistance: Multilingual Style Embeddings and their Evaluation

Justin Qiu, Jiacheng Zhu, Ajay Patel +2

Style embeddings are useful for stylistic analysis and style transfer; however, only English style embeddings have been made available. We introduce Multilingual StyleDistance (mSt…

cs.CL2025

StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples

Ajay Patel, Jiacheng Zhu, Justin Qiu +4

Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. However, the contrastive triplets oft…

cs.CL2024

TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings

Zachary Horvitz, Ajay Patel, Kanishk Singh +3

The goal of text style transfer is to transform the style of texts while preserving their original meaning, often with only a few examples of the target style. Existing style trans…