arXiv Machine Learning

Selecting Open-Weight Language Models for Zero-Shot Intent Classification: A Systematic Evaluation of 41 Models

arXiv:2607. 27421v1 Announce Type: cross Abstract: Intent classification is a core component of task-oriented dialogue systems, yet practitioners have limited systematic guidance for selecting deployable open-weight language models under compute, latency, and robustness constraints.

arXiv AI

Aug 12

A Cost-Efficient Routing Pipeline for Multilingual Short-Text Classification Using Small Language Models

arXiv:2608. 10939v1 Announce Type: cross Abstract: Multilingual short-text classification supports operational systems such as content moderation, customer support routing, and intent recognition, yet aggregate evaluation often hides large differences between high-resource and low-resource languages.

By Wajdi Ben Saad, Safa Madiouni

llms nlp fine-tuning benchmarks

arXiv AI

Jul 31

DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English

arXiv:2601. 22888v4 Announce Type: replace-cross Abstract: More than 80% of the 1.

By Jio Oh, Paul Vicinanza, Thomas Butler, Steven Euijong Whang, Dezhi Hong, Amani Namboori

llms benchmarks data-engineering

arXiv Machine Learning

Jun 5

Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data

arXiv:2606. 05781v1 Announce Type: new Abstract: Deploying frontier large language models (LLMs) for domain-specific structured evaluation tasks often incurs substantial latency, cost, and data privacy overhead.

By Srinivasan Manoharan, Dilipkumar Nallusamy, Sachin Kumar, Haifeng Wu

llms fine-tuning safety

Hugging Face Trending Papers

Jun 21

Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction

Large language models (LLMs) achieve strong relation extraction (RE), but their computational demands and reliance on proprietary APIs limit deployment in resource-constrained or privacy-sensitive settings. We investigate how far small language models (SLMs) can close this gap across general-domain and literary text.

llms fine-tuning benchmarks safety

arXiv AI

Aug 12

SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information

arXiv:2608. 10692v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps) to complete user instructions.

By Junjie Ye, Zhuohui Sheng, Shaofan Liu, Yulun Zhu, Wenjie Fu, Dingwei Zhu, Ming Zhang, Yujiong Shen, Weichao Wang, Xin Zhao, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang, Pluto Zhou

llms benchmarks

arXiv AI

Jun 2

NILC: Discovering New Intents with LLM-assisted Clustering

arXiv:2511. 05913v2 Announce Type: replace-cross Abstract: New intent discovery (NID) seeks to recognize both new and known intents from unlabeled user utterances, which finds prevalent use in practical dialogue systems.

By Hongtao Wang, Renchi Yang, Wenqing Lin

llms rag benchmarks

arXiv AI

Jul 7

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language

arXiv:2607. 03882v1 Announce Type: cross Abstract: LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilistic information in natural language.

By Diego Cerda-Mardini, Sarath Chandar, Sreenath Madathil

llms

arXiv AI

Jun 3

Efficient ASR Training with Conversations that Never Happened

arXiv:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.

By M\'at\'e Gedeon, P\'eter Mihajlik

llms multimodal benchmarks

arXiv Machine Learning

Jun 25

Shared Doubt: Zero-Shot Cross-Lingual Confidence Estimation for Language Models

arXiv:2605. 31220v2 Announce Type: replace-cross Abstract: Confidence estimation (CE), i.

By Athina Kyriakou, Dennis Ulmer, Ivan Titov

llms nlp

arXiv Machine Learning

Jun 3

From Script to Semantics: Prompting Strategies for African NLI

arXiv:2606. 03304v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly evaluated in multilingual settings, yet their inference behavior in low-resource African languages remains underexplored especially under pure prompting without fine-tuning.

By Anuj Tiwari, Terry Oko-odion, Hannah Nwokocha

llms fine-tuning benchmarks

arXiv AI

Aug 11

Beyond Naturalness: Probing Automated Text-To-Speech Evaluators on Linguistically Grounded Dimensions

arXiv:2608. 09930v1 Announce Type: cross Abstract: Automated Text-to-Speech (TTS) evaluation methods (Mean Opinion Score (MOS) predictors and Audio Large Language Models (Audio-LLM) judges) are expected to reflect human perception, yet it is unclear how well they capture the distinct aspects of speech that listeners actually perceive.

By Oluwanifemi Bamgbose, Simon Rosen, Jash Shah, Lindsay Devon Brin, Hoang H Nguyen, Anke Koelzer, Rachel Hansen, Tara Bogavelli, Fanny Riols

llms multimodal benchmarks

Hugging Face Trending Papers

Jun 2

From Script to Semantics: Prompting Strategies for African NLI

Large language models (LLMs) are increasingly evaluated in multilingual settings, yet their inference behavior in low-resource African languages remains underexplored especially under pure prompting without fine-tuning. We present a systematic study of prompting strategies for Natural Language Inference (NLI) in Swahili, Yoruba, and Hausa using the AfriXNLI benchmark.

llms fine-tuning benchmarks

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