Papers with fine-grained model
IdentifyMe: A Challenging Long-Context Mention Resolution Benchmark for LLMs (2025.naacl-short)
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| Challenge: | Recent evaluations of LLMs on coreference resolution have revealed that traditional output formats and evaluation metrics do not fully capture the models’ referential understanding. |
| Approach: | They propose a benchmark for mention resolution presented in a multiple-choice question format and a curated mixture of different mention types and corresponding entities. |
| Outcome: | The proposed model achieves 81.9% accuracy while the open model achieve 80%. |
Impromptu Cybercrime Euphemism Detection (2025.coling-main)
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| Challenge: | Existing methods for detecting euphemisms are ineffective in impromptu euphorism detection . Existing approaches for e-mail detection are limited to word-level ephemismals . |
| Approach: | They propose a framework for impromptu euphemism detection that integrates context augmentation and multi-round iterative training to better predict the actual meaning of a masked token. |
| Outcome: | The proposed framework improves 76-fold over the previous state-of-the-art euphemism detector. |
GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection? (2024.lrec-main)
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| Challenge: | HateCheck test cases are generic and have simplistic sentence structures that do not match the real-world data. |
| Approach: | They propose a framework to generate more diverse and realistic functional tests from scratch by instructing large language models. |
| Outcome: | The proposed framework generates more diverse and realistic functional tests from scratch by instructing large language models (LLMs). |
FineRAG: Fine-grained Retrieval-Augmented Text-to-Image Generation (2025.coling-main)
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| Challenge: | Recent advances in text-to-image generation still exhibit limitations in terms of knowledge access. |
| Approach: | They propose a fine-grained retrieval-augmented image generation model that breaks down the retrieval task into four critical stages: query decomposition, candidate selection, retrieval augmented diffusion, and self-reflection. |
| Outcome: | The proposed method significantly reduces noise associated with retrieval-augmented image generation and performs better in complex, open-world scenarios. |
Rethinking Depression Prediction from a Fine-Grained Subscore Modeling Perspective via Multi-Task Learning (2026.acl-long)
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| Challenge: | Existing methods for depression assessment rely on standardized ratings, but they are time-consuming and subject to inter-rater variability. |
| Approach: | They propose a fine-grained model for subscore prediction via multi-task learning that can be used to predict depression severity using multiple tasks. |
| Outcome: | The proposed model outperforms baselines and Qwen3-14B direct scoring on the public E-DAIC dataset and to a large-scale private clinical dataset. |