Papers by Fanxu Meng
Law in Silico: Simulating Legal Society with LLM-Based Agents (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are powerful tools for legal simulation, but their application remains underexplored. |
| Approach: | They propose a unified LLM-based agent framework for simulating legal scenarios . they calibrate agent behaviors against real-world crime data . |
| Outcome: | The proposed framework calibrates agent behaviors against real-world crime data. |
HD-PiSSA: High-Rank Distributed Orthogonal Adaptation (2025.emnlp-main)
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| Challenge: | Existing methods for large language models constrain update to low-rank subspaces, limiting expressiveness and performance. |
| Approach: | They propose a distributed PEFT approach that initializes adapters across different devices and aggregates their delta updates collectively on (W) Empirically, HD-PiSSA provides 16 higher effective updated ranks than data-parallel LoRA or PiSSA when fine-tuning on 8 GPUs with the same per-device adapter rank. |
| Outcome: | Empirically, HD-PiSSA outperforms LoRA and PiSSA in math, code, and multi-task learning tasks. |
AutoDSL: Automated domain-specific language design for structural representation of procedures with constraints (2024.acl-long)
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| Challenge: | Domain-specific Language (DSL) is an effective tool to express constraints structurally, but requires case-by-case hand-crafting. |
| Approach: | They propose a framework to automate domain-specific language constraint design . they propose 'autoDSL' framework to optimize syntactic and semantic constraints . |
| Outcome: | The framework automates constraint design across domains and abstracts semantic constraints. |