When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing dense retrieval methods have achieved notable progress, but their effectiveness in legal case retrieval remains limited. |
| Approach: | They propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. |
| Outcome: | The proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, especially when powered by a high-capacity core LLM. |
Similar Papers
EvolveSearch: An Iterative Self-Evolving Search Agent (2025.emnlp-main)
Copied to clipboard
Ding-Chu Zhang, Yida Zhao, Jialong Wu, Liwen Zhang, Baixuan Li, Wenbiao Yin, Yong Jiang, Yu-Feng Li, Kewei Tu, Pengjun Xie, Fei Huang
| Challenge: | Existing approaches to enabling LLM web search proficiency struggle with data production in open-search domains, while supervised fine-tuning struggles with data utilization efficiency. |
| Approach: | They propose an iterative self-evolution framework that combines SFT and RL to enhance agentic web search capabilities without external human-annotated reasoning data. |
| Outcome: | EvolveSearch achieves 4.7% improvement over current state-of-the-art in seven benchmarks . supervised fine-tuning struggles with data production in open-search domains compared with RL . |
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent large language models like GPT-4 have demonstrated astonishing zero-shot capabilities in general domain tasks, but they often generate content with hallucinations in specific domains such as Chinese law. |
| Approach: | They propose a framework for adapting large language models (LLMs) to Chinese legal domains by reformulating generation as an adapt-retrieve-revise process. |
| Outcome: | The proposed framework outperforms existing models in the Chinese legal domain by +33.6 points in the zero-shot setting. |
Logic Rules as Explanations for Legal Case Retrieval (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent efforts to learn explainable legal case retrieval models fail to provide faithful and interpretable explanations for legal cases. |
| Approach: | They propose a framework that uses logic rules to explain legal case retrieval results . they extend benchmarks of LeCaRD and ELAM with manually annotated logic rules . |
| Outcome: | The proposed framework is able to provide faithful explanations for legal case retrieval. |
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation (2025.coling-main)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models have demonstrated remarkable performance across tasks. |
| Approach: | They propose a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models. |
| Outcome: | The proposed framework extends existing benchmarks to extend models across tasks and tasks. |
A Self-Evolving LLM Agent Framework for Role-Based Norm Compliance in Healthcare (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing systems treat roles as static prompts and rely on one-shot safety filters . a self-evolving LLM agent is proposed that learns from role-based social experience . |
| Approach: | They propose a self-evolving LLM agent that learns from role-based social experience and explicitly models communicator-level individual traits informed by prior communication questionnaires and clinical literature. |
| Outcome: | The proposed agent learns from role-based social experience and models communicator-level individual traits informed by prior communication questionnaires and clinical literature. |
Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for legal case retrieval often overlook the incorporation of legal expert knowledge, leading to unsatisfactory retrieval performance. |
| Approach: | They propose a legal knowledge-guided case reformulation approach based on large language models for effective and interpretable legal case retrieval. |
| Outcome: | The proposed model performs better on complex legal case queries than existing methods. |
Legal Case Retrieval: A Survey of the State of the Art (2024.acl-long)
Copied to clipboard
| 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. |
Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing large language models (LLMs) underperform in legal judgment prediction due to challenges in understanding case facts and distinguishing between similar charges. |
| Approach: | They propose a framework that allows LLMs to discriminate among charges and a judicial reasoning framework to improve their models for effective legal judgment prediction. |
| Outcome: | The proposed framework improves accuracy and efficiency when dealing with complex and confusing charges. |
Beyond Static Toolsets: Self-Evolving LLM Tool Agents via Continual Documentation Adaptation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for evaluating tool usage assume static toolsets with fixed APIs and documentation. |
| Approach: | They propose a continual documentation adaptation framework that allows LLM agents to self-evolve by updating tool documentation. |
| Outcome: | The proposed framework improves performance on three evolution patterns on dynamic extensions of StableToolBench and RestBench. |
LLM-Evolve: Evaluation for LLM’s Evolving Capability on Benchmarks (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing benchmarks for large language models evaluate LLMs on i.i.d. tasks, overlooking their ability to learn iteratively from past experiences. |
| Approach: | They propose a framework which extends established benchmarks to sequential problem-solving settings and provides feedback after each round to build a demonstration memory that the models can query in future tasks. |
| Outcome: | The proposed framework can improve performance of LLMs by learning from past interactions and improve models' performance over time. |