Papers with LCR
CiteBART: Learning to Generate Citations for Local Citation Recommendation (2025.emnlp-main)
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| Challenge: | Local citation recommendation (LCR) suggests a set of papers for a citation placeholder in a given context. |
| Approach: | They introduce a citation-specific pre-training framework where author-date citation tokens are masked to learn to reconstruct them to fulfill local citation recommendation (LCR). |
| Outcome: | The proposed model achieves state-of-the-art performance on LCR benchmarks except for the FullTextPeerRead dataset, which is quite small to see the advantage of generative pre-training. |
LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation (2025.emnlp-main)
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| Challenge: | Existing studies on legal case retrieval have limited results . limited representations and legally irrelevant matches are often used . |
| Approach: | They propose a large-scale Korean LCR benchmark and a retrieval model that performs legal element reasoning over the query case. |
| Outcome: | a new model outperforms baseline models on a Korean LCR benchmark . it performs state-of-the-art on 411 diverse crime types in queries over 1.2M candidate cases . previous studies have shown that the model can generalize to out-of domain cases if it is trained on in-domain data . |
Legal Case Retrieval: A Survey of the State of the Art (2024.acl-long)
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| Challenge: | Recent years have seen increasing attention on Legal Case Retrieval (LCR) this task involves retrieving cases from a legal database of historical cases that are similar to a given query case. |
| Approach: | They present a survey of the major milestones made in legal case retrieval research . they seek to understand the datasets and recent neural models and their performances . |
| Outcome: | The proposed task is based on a dataset of historical cases similar to a given query case. |
Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs (2024.emnlp-main)
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| Challenge: | Existing studies focus on case-to-case retrieval using lengthy queries, which does not match real-world scenarios. |
| Approach: | They propose a method to construct query-candidate pairs and build the largest LCR dataset to date, LEAD. |
| Outcome: | Experimental results show that the method can provide ample training signals for LCR models. |
LCR-RAG: Enhancing Logical Consistency in Retrieval-Augmented Generation via Neuro-symbolic Reinforcement Learning (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) is widely used to ground large language models in external knowledge and improve factual accuracy. |
| Approach: | They propose a framework that integrates neuro-symbolic verification with reinforcement learning to optimize logical consistency. |
| Outcome: | The proposed framework outperforms strong RAG baselines on hotpotQA, ASQA, and TriviaQA. |
GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval (2026.acl-long)
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| Challenge: | Existing dense retrieval methods neglect the explicit legal logic that underpins legal relevance. |
| Approach: | They propose a framework that reformulates retrieval as an inference process over latent legal variables. |
| Outcome: | GLIER outperforms strong baselines like SAILER and KELLER in a legal case-based retrieval task . the framework exhibits exceptional data efficiency even when trained with only 10% of the data . |