REVIEW 27 cited by
SaulLM-7B: A pioneering Large Language Model for Law
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain. With 7 billion parameters, SaulLM-7B is the first LLM designed explicitly for legal text comprehension and generation. Leveraging the Mistral 7B architecture as its foundation, SaulLM-7B is trained on an English legal corpus of over 30 billion tokens. SaulLM-7B exhibits state-of-the-art proficiency in understanding and processing legal documents. Additionally, we present a novel instructional fine-tuning method that leverages legal datasets to further enhance SaulLM-7B's performance in legal tasks. SaulLM-7B is released under the MIT License.
Forward citations
Cited by 27 Pith papers
-
Citation Grounding: Detecting and Reducing LLM Citation Hallucinations via Legal Citation Graphs
Citation Grounding metric and CG-DPO training method detect and reduce hallucinations in LLM-generated legal citations using a graph from 100.8 million court decisions.
-
From Talking Words to Sharing Thoughts: Scalable Multi-LLM Aggregation via Structured Message Passing
A bipartite factor graph with message-passing protocol and asymmetric damping aggregates multi-LLM predictions, cutting token use by 97% and API calls by 6X while outperforming baselines on MMLU, MMLU-Pro, GPQA, and MedMCQA.
-
VertMark: A Unified Training-Free Robust Watermarking Framework for Vertical Domain Pre-trained Language Models
VertMark embeds robust, training-free watermarks into vertical domain language models by creating hidden semantic equivalence between low-frequency triggers and high-frequency domain terms via parameter swaps, support...
-
NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO
Solver-verified NormWorlds-CF and MR-GRPO show that answer-only training is an unsafe proxy and that class-conditioned metamorphic rewards improve balanced counterfactual change structure.
-
NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO
NormWorlds-CF provides solver-verified normative reasoning tasks without LLM judges; answer-only RL saturates verdicts but not falsification, and class-conditioned GRPO improves some structural change fields.
-
DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices
DECO is a sparse MoE architecture with ReLU-based routing, learnable expert scaling, and NormSiLU activation that matches dense Transformer performance at 20% expert activation and delivers 2.93x speedup on Jetson AGX Orin.
-
DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices
DECO matches dense model performance at 20% expert activation via ReLU-based routing with learnable scaling and the NormSiLU activation, plus a 3x real-hardware speedup.
-
DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices
DECO sparse MoE matches dense Transformer performance at 20% expert activation with a 3x hardware inference speedup.
-
EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagation
EvoRAG adds a feedback-driven backpropagation step that attributes response quality to individual knowledge-graph triplets and updates the graph to raise reasoning accuracy by 7.34 percent over prior KG-RAG methods.
-
OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning
OpsLLM's pipeline (HITL data curation, SFT, GRPO RL with a domain process reward model) improves LLM accuracy on software-operations QA and RCA, especially on in-distribution root-cause-analysis tasks.
-
Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization
CAMEL is a scaling law capturing nonlinear model-size and mixture interactions to extrapolate optimal data mixtures for large LLMs from small-model experiments, reducing optimization cost by 50% and improving benchmar...
-
Retrieval-Augmented Generation for Natural Language Processing: A Survey
The survey organizes RAG methods via a taxonomy of query-based, logits-based, latent, and parametric fusion with comparisons on accessibility, efficiency, applications, and challenges.
-
GradeLegal: Automated Grading for German Legal Cases
Reasoning-oriented LLMs reach up to 0.91 quadratic weighted kappa agreement with experts on public law cases when given sample solutions and grading rubrics, but only 0.60 on criminal law cases.
-
A Few Good Clauses: Comparing LLMs vs Domain-Trained Small Language Models on Structured Contract Extraction
Domain-trained small language model Olava Extract outperforms frontier LLMs on structured contract extraction with macro F1 0.812, micro F1 0.842, highest precision, and 78-97% lower inference cost.
-
ChipLingo: A Systematic Training Framework for Large Language Models in EDA
ChipLingo trains LLMs on EDA data via corpus construction, domain-adaptive pretraining, and RAG scenario alignment, reaching 59.7% accuracy with an 8B model and 70.02% with a 32B model on a new internal EDA benchmark.
-
OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning
OpsLLM outperforms general LLMs on software operations QA and RCA tasks through human-in-the-loop data curation, supervised fine-tuning, and domain-specific reinforcement learning.
-
LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents
On CUAD legal contracts, a prompt-engineered QWEN-2 pipeline with chunking and two answer-selection heuristics reportedly outperforms the fine-tuned DeBERTa-large baseline by about 9%, reaching claimed state-of-the-ar...
-
TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Law
Trident-Bench provides 2,652 professionally validated harmful prompts across finance, law, and medicine, and shows that domain-specialized LLMs often comply with unethical requests more than generalist models.
-
Maat: The Agentic Legal Research Assistant for Competition Protection
Maat is a ReAct agent that orchestrates tools and RAG for competition law research, outperforming baselines on case-specific tasks while providing official citations.
-
Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective
Reliable AI needs structured Knowledge Objects to externalize and enable human validation of implicit knowledge that current methods cannot verify.
-
OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning
OpsLLM is a domain-specific LLM for software ops QA and RCA built with human-curated data, SFT, and RL using a domain process reward model, showing accuracy gains of 0.2-5.7% on QA and 2.7-70.3% on RCA over general LLMs.
-
Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning
An iterative data-optimization pipeline that simplifies, extends, and rewrites SFT examples based on the model's own loss, embedding sparsity, and self-scores reports up to 7.15 absolute points of average benchmark im...
-
Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report
Foundation-Sec-8B-Instruct, an instruction-tuned 8B cybersecurity LLM, is released and claimed to beat Llama 3.1-8B-Instruct on CTIBench-RCM and CTIBench-MCQA while remaining competitive on general instruction-following.
-
AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions
The paper introduces a taxonomy of AI safety for LLMs organized into Trustworthy AI, Responsible AI, and Safe AI perspectives, accompanied by a review of state-of-the-art methods, challenges, and future directions.
-
Position: Hippocampal Explicit Memory Is the Cornerstone for AGI
Explicit memory modeled on the hippocampus is the cornerstone needed to advance LLMs to AGI because their implicit statistical learning cannot produce higher cognitive functions.
-
Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective
Proposes Knowledge Objects to externalize implicit AI knowledge for human verification, addressing a gap in current reliability methods.
-
Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics
LLM pipeline for joint post-level assessment and user-level temporal modeling of mental health from ordered social media posts in a shared task.
Discussion (0). Sign in to comment.