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Notes on natural language processing
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ContractScrub: A benchmark for final review of legal contracts
Researchers introduce ContractScrub, a new benchmark to evaluate the performance of language models in contract scrubbing, which is the final review of legal contracts for errors and inconsistencies. The study finds…
ContractScrub: A benchmark for final review of legal contracts
The paper introduces ContractScrub, a new benchmark for evaluating the performance of Language Models (LLMs) in reviewing legal contracts for errors. The study finds that while LLMs perform well on general benchmarks…
Interrupting the Loop: Periodic Subject Changes Raise Judged Surprise and Connection in Base Language Models
The research examines where novelty comes from in base language models and what they can perceive in a long text stream. The study dismantles a cognitively inspired generation loop across 24 conditions on three base…
Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
Researchers have developed a method called Task-CoEvolve to improve the efficiency of harness optimization for large language models (LLMs). This method co-evolves validation tasks with the harness by focusing on tasks…
Training-Free Inference-Time Self-Reflection and Cost-Bounded Early Stopping for Large Language Models
The paper presents EvoResearcher, a training-free protocol for adding cost-bounded self-reflection to large language models (LLMs). The protocol is designed to work with a single frozen LLM backbone and aims to improve…
ACL Anthology team
The ACL Anthology is a collection of papers related to natural language processing, published by the ACL Anthology team. Papers from 2016 onwards are licensed under Creative Commons Attribution 4.0 International…
Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams
The paper discusses Diagram-MMU, a new benchmark designed to evaluate the ability of Multimodal Large Language Models (MLLMs) in parsing and understanding scientific diagrams. The benchmark includes 3.7k diagrams and…
GitSkills: A Dataset of Agent Skills on GitHub
Researchers present GitSkills, a dataset containing over 3 million skill files for language-model agents found in public GitHub repositories in July 2026. The dataset retains every file occurrence with its repository…
CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation
The authors introduce a framework called CARD for generating realistic credit card discussion threads on online platforms, such as Reddit. The framework takes into account various factors like reply structure, comment…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
The authors propose a reference-free framework to evaluate the quality of task-oriented conversational agent benchmarks, which are often used to assess these agents. The framework uses Large Language Models (LLMs) as…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The authors propose a new method for document search called READ (Reliable Embedding-free Agentic Document-search). This method uses an interpretable agent that performs three operations: normalized lexical search…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The paper introduces a new approach called READ (Reliable Embedding-free Agentic Document-search) for retrieving information from long documents such as financial statements and audit reports. The traditional method of…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The paper proposes a new approach for document search called 'READ (Reliable Embedding-free Agentic Document-search)', which aims to address the issues with current black-box retrieval designs in handling long…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The authors propose a new method for document search called Reliable Embedding-free Agentic Document-search (READ). In contrast to the common design of retrieval-augmented generation, READ uses an agent that reads the…
Schema-Guided Hierarchical Information Extraction and Semantic Evaluation Using Generative AI
Researchers have developed a new method for extracting complex, structured information from unstructured text documents using generative AI. The method involves creating a schema that serves as an information model…
Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
TYTAN is a system that automatically constructs an analytic semantic schema from a relational database, with user-provided descriptions when available. It uses symbolic analysis and LLM-based semantic inference for…
OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling
In this research, a new pipeline called OctoLong is introduced to enhance the training of language models (LM) by providing them with long code contexts. This is done through the use of an AST parser, a language server…
OPD-V: Visual On-Policy Self-Distillation with Modality Balance
The paper introduces a new method called On-Policy Self-Distillation with Visual Modality Balance (OPD-V) to improve the visual reasoning of multimodal large language models. The authors argue that previous methods…
EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
The paper introduces EMBL AI Librarian, a system that enhances the Europe PMC interface for AI agents to enable them to search and retrieve evidence in natural language. The system was evaluated across four benchmarks…
DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
The paper proposes a framework called DataOrchestra that learns to curate pre-training data example-by-example rather than applying a uniform strategy across many examples. The framework decides whether to drop, clean…
Making Mathematical Knowledge Explainable, Accessible and Interoperable Through Large Language Model Integration
The authors propose integrating Large Language Models (LLMs) with a mathematical knowledge base called MathModDB to improve its accessibility, explainability, and interoperability. The integration is achieved through a…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The authors introduce GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating improvements across multiple agents in the pipeline, rather than optimizing components in isolation…
GS-Agent: Creating 4D Physical Worlds With Generative Simulation
Researchers have developed GS-Agent, a system that uses natural language descriptions to automatically generate realistic, dynamic, and controllable 4D physical worlds. This is achieved through an end-to-end…
GS-Agent: Creating 4D Physical Worlds With Generative Simulation
Researchers have developed GS-Agent, a system that generates realistic, dynamic, and controllable 4D physical worlds from natural language descriptions using an end-to-end multi-agent framework. The system emulates…
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