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cs.CL2026
KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration
Mohammad Amanlou, Erfan Shafiee Moghaddam, Yasaman Amou Jafari +3
With the rise of large language models (LLMs), they have become instrumental in applications such as Retrieval-Augmented Generation (RAG). Yet evaluating these systems remains bott…
cs.CL2026
The Sufficiency-Conciseness Trade-off in LLM Self-Explanation from an Information Bottleneck Perspective
Ali Zahedzadeh, Behnam Bahrak
Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering. While these explanations…
cs.CL2025
Can LLMs Faithfully Explain Themselves in Low-Resource Languages? A Case Study on Emotion Detection in Persian
Mobina Mehrazar, Mohammad Amin Yousefi, Parisa Abolfath Beygi +1
Large language models (LLMs) are increasingly used to generate self-explanations alongside their predictions, a practice that raises concerns about the faithfulness of these explan…