Papers with black-box framework
DPDLLM: A Black-box Framework for Detecting Pre-training Data from Large Language Models (2024.findings-acl)
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Baohang Zhou, Zezhong Wang, Lingzhi Wang, Hongru Wang, Ying Zhang, Kehui Song, Xuhui Sui, Kam-Fai Wong
| Challenge: | Existing methods to detect pretraining data from large language models are unrealistic to them. |
| Approach: | They propose to detect pre-training data from LLM in a black-box way by using GPT-2 as reference model and feed it with sequence probabilities to detect whether it was used to train it. |
| Outcome: | The proposed framework outperforms existing methods on the benchmark datasets and shows that it is effective on different popular LLMs. |
Automatic Prompt Engineering for Scalable Prompt Inversion in Text-to-Image Ad Generation (2026.acl-industry)
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| Challenge: | PRISM-DUEL is a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE) PRIMS-DUEl is motivated by advertising workflows requiring low-latency, diverse variants faithful to a human-designed ad. |
| Approach: | They propose a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE) they obtain label-free pairwise preferences and rationales from an LLM judge over pairs of generated images and use a dueling-bandit optimizer to optimize a prompt for generating controlled variations while matching the reference ad's visual content. |
| Outcome: | The proposed framework preserves visual similarity and semantic faithfulness while increasing diversity. |
Zero-knowledge LLM hallucination detection and mitigation through fine-grained cross-model consistency (2025.emnlp-industry)
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| Challenge: | Existing methods for hallucination management fail to integrate both detection and mitigation without external knowledge sources. |
| Approach: | They propose a black-box framework that leverages fine-grained cross-model consistency to detect and mitigate hallucinations in LLM outputs without external knowledge sources. |
| Outcome: | The proposed framework improves hallucination detection scores by 6-39% on a FELM dataset . it achieves 9 percentage points improvement in answer accuracy on the GPQA-diamond dataset compared to existing approaches . |
RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data (2023.emnlp-main)
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| Challenge: | Out-of-distribution (OOD) detection is a widely covered topic in classification tasks, but most methods rely on hidden features output by the encoder. |
| Approach: | They propose to leverage soft-probabilities in a black-box framework to detect OOD . they propose to use a more operational evaluation setting to enable OOD detection . |
| Outcome: | The proposed framework can access soft-predictions but not the internal states of the model. |
Exploiting Prompt-induced Confidence for Black-Box Attacks on LLMs (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) are vulnerable to adversarial attacks even in strict black-box settings with only hard-label feedback. |
| Approach: | They propose a black-box framework that leverages prompt-induced confidence as an auxiliary signal to guide attacks. |
| Outcome: | The proposed framework improves the attack success rate and query efficiency while maintaining semantic fidelity. |
STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are susceptible to jailbreak prompts that can elicit harmful or inappropriate responses. |
| Approach: | They propose a black-box framework for automated red teaming that integrates a Multi-Agent System with a Strategy-Response Multiplex Network and employs network-driven optimization to sample effective attack strategies. |
| Outcome: | The proposed framework surpasses existing methods and achieves higher attack success rate (ASR) at lower computational cost. |
Hallucination Detection in Long-Form Text Generated by LLMs: A Benchmark and a Hyper-Relational Knowledge Graph Approach (2026.findings-acl)
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| Challenge: | Existing methods for hallucination detection are coarse-grained and lack long-range consistency checks. |
| Approach: | They propose a benchmark for long-form hallucination detection that incorporates diverse entity types and intricate factual dependencies spanning extended contexts. |
| Outcome: | The proposed framework outperforms baselines and robustly integrates fact-centric hyper-relational knowledge graphs. |