Papers with FAR
AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor (2026.findings-acl)
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| Challenge: | AutoMonitor-Bench evaluates the reliability of LLM-based misbehavior monitors across diverse tasks and failure modes. |
| Approach: | They introduce AutoMonitor-Bench, a benchmark designed to evaluate misbehavior monitors across diverse tasks and failure modes. |
| Outcome: | The new benchmark evaluates the reliability of LLM-based misbehavior monitors across tasks and failure modes. |
Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems (2021.acl-long)
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Claudio Pinhanez, Paulo Cavalin, Victor Henrique Alves Ribeiro, Ana Appel, Heloisa Candello, Julio Nogima, Mauro Pichiliani, Melina Guerra, Maira de Bayser, Gabriel Malfatti, Henrique Ferreira
| Challenge: | a recent study shows that meta-knowledge embedded in intent identifiers improves intent recognition in conversational systems . a meta-learning approach is used to classify sentences into discrete sets of classes . classification is a key part of professional conversational system implementations . |
| Approach: | They use meta-knowledge embedded in intent identifiers to improve intent recognition . authors found that meta-knowledge improved accuracy in conversational systems . |
| Outcome: | The meta-knowledge enabled improved intent recognition in conversational systems . the meta-learning improved the false acceptance rate in two thirds of the chatbots . |
Fair Federated Learning with Biased Vision-Language Models (2024.findings-acl)
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| Challenge: | Existing literature ignores the inherent group unfairness within CLIP and its ethical implications on FL applications. |
| Approach: | They propose a fairness-aware adaptation framework for CLIP in federated learning . they propose to leverage biased pre-trained VLMs to build fair FL frameworks . |
| Outcome: | The proposed framework addresses unique bias in FL, triggered by data heterogeneity . it trains a fair FL model with fairness-aware deep visual prompting (DVP) Extensive results on human face attribute recognition (FAR) applications show it outperforms state-of-the-art FL models . |
SAFER: A Controllable Safeguard for LLMs against Backdoor Attacks (2026.findings-acl)
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| Challenge: | Existing inference-time defenses lack explicit control over false acceptance rate (FAR) existing inference time defenses aim to mitigate poisoned inputs but lack explicit FAR control . |
| Approach: | They propose a framework that provides explicit control over false acceptance rate without prior knowledge of backdoor samples. |
| Outcome: | The proposed framework outperforms existing inference-time defenses on three benchmark datasets . it provides explicit and provable control over false acceptance rate without prior knowledge of backdoor samples . |
PAR: Training-Free Positional Perturbation and Attention Recycling for Faithful OCR (2026.acl-long)
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| Challenge: | In high-precision tasks, vision language models suffer from Linguistic Priors Hallucination . |
| Approach: | They propose a training-free, inference-time intervention framework to mitigate this by integrating visual encoders with Large Language Model decoders. |
| Outcome: | The proposed framework reduces hallucination rates by 12% in long-context scenarios while maintaining robust generalization on standard benchmarks. |