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cs.CL2026
EffComp: Efficient Prompt Compression via Hybrid Reinforcement & Supervised Learning
Romtham Tungsununtham, Piyawat Lertvittayakumjorn, Atiwong Suchato +1
Prompt compression is aimed at reducing input prompt lengths to enable cheaper, faster, and potentially higher-quality LLM predictions. However, existing prompt compression methods…
cs.CL2026
What Limits Us? Analyzing Self-Reported Limitations in NLP Research
Tawan Thaepprasit, Peeranuth Kehasukcharoen, Ding Wang +3
Since late 2022, a Limitations section has become mandatory at many top-tier NLP conferences. The growing number of accepted papers at these venues has resulted in a vast corpus of…