Papers with removal
RAGViz: Diagnose and Visualize Retrieval-Augmented Generation (2024.emnlp-demo)
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| Challenge: | Large language models (LLMs) lack domain-specific knowledge and can cause hallucinations. |
| Approach: | They propose a RAG diagnosis tool that visualizes the attentiveness of the generated tokens in retrieved documents. |
| Outcome: | RAGViz provides token and document-level attention visualization and generation comparison upon context document addition and removal. |
Which Retain Set Matters for LLM Unlearning? A Case Study on Entity Unlearning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are prone to retaining unauthorized or sensitive information from their training data, which raises privacy concerns. |
| Approach: | They propose to use a group of queries that share similar syntactic structures with the data targeted for removal to investigate the effects of unlearning on various subsets of the retain set. |
| Outcome: | The proposed method reduces the retention set, the portion of training data that is not targeted for removal, and improves model performance across subsets. |
Can Indirect Prompt Injection Attacks Be Detected and Removed? (2025.acl-long)
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| Challenge: | Recent studies have developed various detection mechanisms to protect against prompt injection attacks. |
| Approach: | They investigate the feasibility of detecting and removing indirect prompt injection attacks . they use two methods to evaluate their performance and train detection models . |
| Outcome: | The proposed method is based on a benchmark dataset and is available on github . it evaluates the performance of existing models and open-source detection models . |
Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch (2025.acl-long)
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| Challenge: | To meet the demands of content moderation, online platforms have resorted to automated systems. |
| Approach: | They conduct an audit of Twitch’s automated moderation tool (AutoMod) to investigate its effectiveness in flagging hateful content. |
| Outcome: | The automated moderation tool (AutoMod) is used to filter hateful content on Twitch and send 107,000 comments from 4 datasets. |
RACCooN: Versatile Instructional Video Editing with Auto-Generated Narratives (2025.emnlp-main)
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| Challenge: | Recent video generative models rely on detailed, labor-intensive text prompts for tasks, limiting adaptability for personal/raw videos. |
| Approach: | They propose a video-to-paragraph-to video editing method that supports diverse video editing capabilities, such as removal, addition, and modification, through a unified pipeline. |
| Outcome: | The proposed method supports diverse video editing capabilities, such as removal, addition, and modification, through a unified pipeline. |
Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs (2026.acl-long)
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| Challenge: | Spontaneous speech is rarely fluent, and disfluencies can degrade readability and reliability . a sequence tagger first marks disfluent tokens, and these signals guide instruction fine-tuning . |
| Approach: | They propose a multilingual correction pipeline where a sequence tagger first marks disfluent tokens . they add a contrastive learning objective that penalizes the reproduction of disfluency tokens. |
| Outcome: | The proposed model improves readability and reliability of ASR transcripts in three languages . disfluencies can cause misinterpretations, incoherent responses, poor user experience . |