Topic coverage
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Benchmarks
Most implemented
Neural Topic Modeling with Large Language Models in the Loop
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
MUG: A General Meeting Understanding and Generation Benchmark
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation
Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders
A Topic Coverage Approach to Evaluation of Topic Models
Papers
X+Slides: Benchmarking Audience-Conditioned Slide Generation
Automatically generating slide decks from source documents is an important application of large language models (LLMs). Existing benchmarks primarily assess slide completeness and technical depth, while overlooking the t…
Topic coverageGIScholarBench: Benchmarking LLM Overconfidence in GIS Research
Large language models (LLMs) are increasingly used in academic research workflows, but scholarly tasks require high factual precision and therefore expose a key weakness: overconfidence. Here, overconfidence is defined b…
Topic coverageAgent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding their responses in external knowledge, but conventional pipelines rely on static, single-step retrieval that limits performance on c…
Topic coverageKletterMix: Climbing Toward High-Quality German Pretraining Data - The Full Report
High-quality pretraining data is a central ingredient in modern language models, but German-language resources remain far less developed than their English counterparts: they are often smaller, less carefully curated, we…
Topic coverageASTRA-QA: A Benchmark for Abstract Question Answering over Documents
Document-based question answering (QA) increasingly includes abstract questions that require synthesizing scattered information from long documents or across multiple documents into coherent answers. However, this settin…
Question AnsweringTopic coverageFrom Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors
Long-context large language models remain computationally expensive to run and often fail to reliably process very long inputs, which makes context compression an important component of many systems. Existing compression…
Topic coverage