Papers with CAG
WikiVideo: Article Generation from Multiple Videos (2026.findings-acl)
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Alexander Martin, Reno Kriz, William Gantt Walden, Kate Sanders, Hannah Recknor, Eugene Yang, Francis Ferraro, Benjamin Van Durme
| Challenge: | Existing methods for retrieval-augmented generation focus on text rather than video. |
| Approach: | They propose a benchmark to generate Wikipedia-style articles from multiple videos . they propose 'collaborative article generation' that leverages an r1-style reasoning model and a VideoLLM to draw higher-level inferences about the target event than is possible with VideoLLms alone. |
| Outcome: | The proposed method outperforms existing methods in oracle retrieval and RAG settings while suggesting promising avenues for future work. |
Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph (2023.eacl-demo)
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| Challenge: | Graphs are a natural representation of complex data as their structure allows users to discover (often implicit) relations among the nodes intuitively. |
| Approach: | They propose a corpus annotation graph framework that extends graphs with automatically extracted annotations. |
| Outcome: | The proposed framework can be used for further analyses across multiple downstream tasks. |
Classifier-Augmented Generation for Structured Workflow Prediction (2025.emnlp-industry)
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| Challenge: | a new system translates natural language descriptions into executable workflows . configuring stages and their properties is time consuming and requires deep tool knowledge. |
| Approach: | They propose a system that translates natural language descriptions into executable workflows . it uses a Classifier-Augmented Generation approach that combines utterance decomposition with a classifier and stage-specific prompting to produce accurate stage predictions. |
| Outcome: | The proposed system outperforms existing models and reduces token usage by 60%. |
CAGK: Collaborative Aspect Graph Enhanced Knowledge-based Recommendation (2024.lrec-main)
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| Challenge: | Existing KG-based recommendations have low link rates, redundant knowledge in KG, and low ratings and negative aspect sentiment. |
| Approach: | They propose a model that integrates auxiliary information such as social networks, user or item attributes, images, contextual data, etc. |
| Outcome: | The proposed model improves on two widely used benchmark datasets, Amazon-book and Yelp2018. |
InsideOut: Measuring and Mitigating Insider–Outsider Bias in Interview Script Generation (2026.acl-long)
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| Challenge: | Recent research has raised concerns about culture-related fairness issues in LLM-generated content. |
| Approach: | They propose to use 4,000 generation prompts and three evaluation metrics to quantify LLMs' **insider-outsider bias** . |
| Outcome: | The proposed method reduces bias in Llama model by 89.70% and mitigates bias on Qwen by 82.54% on cultural alignment gap metric. |
Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation (2024.emnlp-main)
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| Challenge: | Existing RAG paradigms suffer from the impact of flawed information introduced during the retrieval phrase, thereby diminishing the reliability and correctness of the generated output. |
| Approach: | They propose a framework that empowers models to discern and process information based on its credibility. |
| Outcome: | The proposed framework outperforms existing models with retrieval augmentation and exhibits robustness despite increasing noise in the context. |
AIM-CoT: Active Information-driven Multimodal Chain-of-Thought for Vision-Language Reasoning (2026.acl-long)
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| Challenge: | Existing methods for I-MCoT fail to capture dynamic needs of vision-language models . existing methods rely on attention signals, which are unreliable under severe granularity imbalance between brief textual query and informative image. |
| Approach: | They propose a framework that integrates specially selected visual evidence into the context of Vision-Language Models (VLMs) they propose 'AIM-CoT' to improve evidence selection and insertion triggering . |
| Outcome: | Experiments across three benchmarks and four backbones demonstrate the proposed framework’s consistent superiority. |