Papers with hybrid framework
Thesis Proposal: An Explainable Multimodal Framework for Detecting Harmful Content in Code-Switched Children’s Media (2026.acl-srw)
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| Challenge: | Current content moderation systems fail to protect children from harmful content, especially in under-resourced, code-switched settings. |
| Approach: | They propose to integrate a fine-tuned classifier with an LLM-powered module that synthesizes the classifier’s internal evidential signals to generate faithful, human-readable rationales for each decision. |
| Outcome: | The proposed framework integrates a fine-tuned classifier for accurate, scalable detection with an LLM-powered module that synthesizes the classifier’s internal evidential signals to generate faithful, human-readable rationales for each decision. |
Enhancing Foundation Models in Transaction Understanding with LLM-based Sentence Embeddings (2025.emnlp-industry)
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| Challenge: | Existing foundation models for tabular transactional data rely on index-based representations for categorical merchant fields. |
| Approach: | They propose a framework that uses LLM-generated embeddings as semantic initializations for lightweight transaction models. |
| Outcome: | The proposed framework improves performance on large transaction datasets. |
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference (2022.tacl-1)
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| Challenge: | Existing explainable multi-hop inference models are regarded as black-boxes due to their ability to transfer linguistic and semantic information to downstream tasks, posing concerns about interpretability and transparency of their predictions. |
| Approach: | They propose a hybrid framework that integrates explicit constraints with neural architectures through differentiable convex optimization to answer and explain multi-hop questions in natural language. |
| Outcome: | The proposed framework improves performance on scientific and commonsense QA tasks while still providing structured explanations in support of its predictions. |
Revealing the Truth with ConLLM for Detecting Multi-Modal Deepfakes (2026.findings-eacl)
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Gautam Siddharth Kashyap, Harsh Joshi, Niharika Jain, Ebad Shabbir, Jiechao Gao, Nipun Joshi, Usman Naseem
| Challenge: | Existing methods for deepfake detection suffer from two limitations: modality fragmentation and shallow inter-modal reasoning. |
| Approach: | They propose a framework for multimodal deepfake detection that uses contrastive learning and large language models to mitigate modality fragmentation and refine embeddings to address shallow inter-modal reasoning. |
| Outcome: | ConLLM reduces audio deepfake EER by 50%, improves video accuracy by 8%, and achieves approximately 9% accuracy gains in audio-visual tasks. |
DRP: Distilled Reasoning Pruning with Mathematical Skill-aware Step Decomposition for Efficient Large Reasoning Models (2026.findings-acl)
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| Challenge: | Existing solutions to this problem are inference-time pruning and tuning-based distillation. |
| Approach: | They propose a framework that combines inference-time pruning with tuning-based distillation to enable efficient and accurate reasoning. |
| Outcome: | The proposed framework reduces token usage while improving accuracy on GSM8K and AIME tokens while avoiding performance drop. |
Hi-ZFO: Hierarchical Zeroth- and First-Order LLM Fine-Tuning via Importance-Guided Tensor Selection (2026.findings-acl)
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| Challenge: | generative tasks require a high degree of exploratory capacity, but zeroth-order methods suffer from slow convergence . generative task-specific methods tend to converge toward local minima, causing noise and inefficient estimation . |
| Approach: | They propose a framework that synergizes FO precision with exploratory capability of ZO estimation. |
| Outcome: | The proposed framework synergizes precision of FO gradients with exploratory capability of ZO estimation. |
Bridging Attribution and Open-Set Detection using Graph-Augmented Instance Learning in Synthetic Speech (2026.eacl-long)
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| Challenge: | Synthetic speech detection is a critical part of safeguarding digital communication, enabling systems to identify and mitigate the risks posed by highly realistic, machine-generated voices. |
| Approach: | They propose a framework that combines SFMs with graph-based modeling and open-set generalization to capture meaningful relationships between utterances and recognize speech that doesn’t belong to any known generator. |
| Outcome: | The proposed framework improves performance across both tasks, with Mamba-based embeddings delivering particularly strong results. |
Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition (L18-1)
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| Challenge: | a new general framework for sign recognition from monocular video is presented . the framework exploits state-of-the-art learning methods while incorporating features based on what we know about the linguistic composition of lexical signs. |
| Approach: | They propose a general framework for sign recognition from monocular video . they exploit state-of-the-art learning methods while incorporating features from linguistic information . |
| Outcome: | The proposed framework exploits state-of-the-art learning methods while incorporating features based on what we know about linguistic composition of lexical signs. |
Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation (2024.findings-acl)
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| Challenge: | Existing methods for simulating social movements encounter challenges in capturing behavior of participants. |
| Approach: | They propose a hybrid framework for social media user simulation wherein users are categorized into two types: core and ordinary users. |
| Outcome: | The proposed framework is able to simulate the behavior of social media users across real-world datasets and demonstrate its effectiveness and flexibility. |
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering (2021.acl-long)
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| Challenge: | Existing generative models for open-domain question answering focus on generating direct answers from unstructured textual information, but a large amount of knowledge is stored in structured databases, and need to be accessed using query languages such as SQL. |
| Approach: | They propose a hybrid framework that takes both textual and tabular evidences as input and generates either direct answers or SQL queries depending on which form could better answer the question. |
| Outcome: | The proposed framework outperforms baseline models on OpenSQuAD datasets and can generate SQL queries on the associated databases to obtain the final answers. |
MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework (2025.findings-emnlp)
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| Challenge: | MedCOD integrates domain-specific structured knowledge into large language models . evaluators evaluated four open-source LLMs with structured prompts . |
| Approach: | They propose a framework that integrates domain-specific structured knowledge into large language models . they constructed a parallel corpus of 2,999 English-Spanish MedlinePlus articles . |
| Outcome: | The proposed framework improves translation quality across four open-source LLMs. |
From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework for Large Language Models (2025.acl-long)
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| Challenge: | Existing watermarking methods face limitations that hinder their effectiveness in diverse and adversarial scenarios. |
| Approach: | They propose a symbiotic watermarking framework with three strategies: serial, parallel, and hybrid. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art (SOTA) performance. |
GASim: A Graph-Accelerated Hybrid Framework for Social Simulation (2026.acl-long)
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| Challenge: | Large-scale social simulators require high latency due to expensive memory retrieval and sequential ABM execution. |
| Approach: | They propose a graph-accelerated hybrid multi-agent framework for large-scale social simulations that uses large language models and numerical agent-based models to scale up simulations. |
| Outcome: | The proposed framework delivers 9.94 speedup over the traditional framework and consumes less than 20% of tokens. |
Bridging Reasoning and Action: Hybrid LLM–RL Framework for Efficient Cross-Domain Task-Oriented Dialogue (2026.findings-acl)
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| Challenge: | Existing methods to solve cross-domain task-oriented dialogues are brittle when cross- domain constraints are not directly grounded in surface text or require commonsense inference. |
| Approach: | They propose a framework that makes LLM-derived constraint reasoning usable for RL. |
| Outcome: | Experiments show that the proposed framework outperforms single-model baselines on long-horizon tasks. |
QAEval: Mixture of Evaluators for Question-Answering Task Evaluation (2025.acl-long)
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| Challenge: | Existing QA evaluation methods struggle with open-ended and unstructured responses. |
| Approach: | They propose a hybrid framework that combines rule-based reliability with LLM-based adaptability to overcome these challenges. |
| Outcome: | The proposed framework outperforms existing models like GPT-4o and Claude-3 in accuracy and cost. |
HAF-RM: A Hybrid Alignment Framework for Reward Model Training (2025.acl-long)
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Shujun Liu, Xiaoyu Shen, Yuhang Lai, Siyuan Wang, Shengbin Yue, Zengfeng Huang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Recent studies have focused on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards. |
| Approach: | They propose a hybrid alignment framework **HAF-RM** that incorporates additional constraint on token-level policy probabilities in addition to the reward score. |
| Outcome: | The proposed framework can supervise the internal preference model at the token level and optimize the mapping layer of the reward model at sequence level. |
Toward Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST) (2025.findings-acl)
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Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona T. Diab
| Challenge: | Despite advances in machine translation, domain-specific terminology translation remains challenging. |
| Approach: | They propose a large-scale multilingual AI terminology dataset that combines LLMs for extraction with human expertise for translation. |
| Outcome: | The proposed framework combines human translation expertise with LLMs to improve translation accuracy and improve BLEU and COMET scores. |
XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake Detection (2026.findings-acl)
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| Challenge: | Advanced speech synthesis technologies have enabled highly realistic speech generation, posing security risks that motivate research into audio deepfake detection (ADD). |
| Approach: | They propose a modular framework that integrates an XLSR front-end with synergistic Mamba-Attention backbones to capture artifacts in spoofed speech signals. |
| Outcome: | The proposed framework achieves competitive performance on the ASVspoof 2021 LA, DF, and In-the-Wild benchmarks compared to other state-of-the art systems. |
Diversity in Unity, Theory in Practice: Hierarchical Multitask Benchmarks for Chinese Minority Languages (2026.acl-long)
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| Challenge: | CMiLBench is a framework to evaluate linguistically and culturally diverse minority languages . rapid evolution of LLMs has revolutionized NLP, but progress is unevenly distributed . |
| Approach: | They propose a framework to translate a theoretical notion of "diversity in unity" into practical evaluation for three minority languages . CMiLBench comprises 24,663 instances across 5 difficulty levels and 17 tasks . |
| Outcome: | The proposed framework evaluates 14 state-of-the-art LLMs with a hybrid framework . it integrates automatic metrics and LLM-as-a-Judge scoring . |