Papers with lightweight, modular framework
Pico: A Modular Framework for Hypothesis-Driven Small Language Model Research (2025.emnlp-demos)
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Richard Diehl Martinez, David Demitri Africa, Yuval Weiss, Suchir Salhan, Ryan Daniels, Paula Buttery
| Challenge: | Recent advances in large language models (LLMs) have enabled strong performance across diverse tasks, but small enough to train on modest budgets. |
| Approach: | They propose a lightweight, modular framework that enables systematic, hypothesis-driven research for small and medium-scale language model development. |
| Outcome: | The proposed framework enables systematic, hypothesis-driven research for small and medium-scale language model development. |
Logic Matters in Lightweight Hallucination Classification for RAG System (2026.acl-long)
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| Challenge: | Existing hallucination detection frameworks for RAGs lack robustness and performance . a compact model may lose track of precise information in retrieved segments or misinterpret a document's entailment score. |
| Approach: | They propose a lightweight, modular framework for hallucination detection in RAG systems . they capture logical relationships among retrieved documents within the vector space . |
| Outcome: | The proposed framework improves hallucination detection in RAG systems without complex architectures or pre-training on datasets. |
CAPSTONE: Composable Attribute‐Prompted Scene Translation for Zero‐Shot Vision–Language Reasoning (2025.emnlp-industry)
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| Challenge: | CAPSTONE transforms visual inputs into structured text prompts that can be interpreted by a frozen Large Language Model (LLM). |
| Approach: | They propose a plug-and-play framework that transforms off-the-shelf vision models into structured text prompts that can be interpreted by a frozen Large Language Model (LLM). |
| Outcome: | The proposed framework outperforms fully trained VLMs on the POPE dataset while the 4B model achieves competitive results. |