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cs.CL2026
Evaluating Prompting Strategies for Chart Question Answering with Large Language Models
Ruthuparna Naikar, Ying Zhu
Prompting strategies affect LLM reasoning performance, but their role in chart-based QA remains underexplored. We present a systematic evaluation of four widely used prompting para…
cs.CL2026
FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation
Siyang He, Qiqi Wang, Xiaoran Liu +8
Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies demonstrate positional bias, failing to fully unlock the potential of ar…
cs.CL2025
RISE: Reasoning Enhancement via Iterative Self-Exploration in Multi-hop Question Answering
Bolei He, Xinran He, Mengke Chen +3
Large Language Models (LLMs) excel in many areas but continue to face challenges with complex reasoning tasks, such as Multi-Hop Question Answering (MHQA). MHQA requires integratin…