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#large language models

On-Policy Self-Distillation for Reasoning Compression

Beginner

Hejian Sang, Yuanda Xu et al.Mar 5arXiv

Reasoning models often talk too much, and those extra words can actually make them more wrong.

#on-policy self-distillation#reasoning compression#conciseness instruction

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MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios

Beginner

Zhiheng Song, Jingshuai Zhang et al.Feb 26arXiv

MobilityBench is a big, carefully built test that checks how well AI helpers can plan real-world routes using natural language and map tools.

#MobilityBench#route-planning agents#large language models

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Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning

Beginner

Yu-Ang Lee, Ching-Yun Ko et al.Feb 4arXiv

When you tune the learning rate carefully, plain old LoRA fine-tuning works about as well as fancy new versions.

#LoRA#parameter-efficient fine-tuning#learning rate tuning

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Enhancing Sentiment Classification and Irony Detection in Large Language Models through Advanced Prompt Engineering Techniques

Beginner

Marvin Schmitt, Anne Schwerk et al.Jan 13arXiv

Giving large language models a few good examples and step-by-step instructions can make them much better at spotting feelings in text.

#prompt engineering#few-shot learning#chain-of-thought

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Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection

Beginner

Zhiwei Liu, Yupen Cao et al.Jan 8arXiv

This paper builds MFMD-Scen, a big test to see how AI changes its truth/false judgments about the same money-related claim when the situation around it changes.

#financial misinformation detection#scenario-induced bias#multilingual benchmark

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