Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models

• URL: http://arxiv.org/abs/2403.11802v3
• Date: Fri, 17 May 2024 16:58:23 GMT
• Title: Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models
• Authors: Mingyang Song, Mao Zheng, Xuan Luo,
• Abstract summary: We propose a benchmark for evaluating long-context Large Language Models (LLMs) named Counting-Stars. We conduct experiments to evaluate long-context LLMs (i.e., GPT-4 Turbo, Gemini 1.5 Pro, Claude3 Opus, GLM-4, and Moonshot-v1) Results show that Gemini 1.5 Pro achieves the best overall results, while GPT-4 Turbo is the most stable across various tasks.
• Score: 14.906150451947443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent research endeavors have focused on developing Large Language Models (LLMs) with robust long-context capabilities, due to the lack of long-context benchmarks, relatively little is known about how well the performance of long-context LLMs. To address this gap, we propose a multi-evidence, position-aware, and scalable benchmark for evaluating long-context LLMs, named Counting-Stars, which evaluates long-context LLMs by using two tasks: multi-evidence acquisition and multi-evidence reasoning. Based on the Counting-Stars test, we conduct experiments to evaluate long-context LLMs (i.e., GPT-4 Turbo, Gemini 1.5 Pro, Claude3 Opus, GLM-4, and Moonshot-v1). Experimental results demonstrate that Gemini 1.5 Pro achieves the best overall results, while the performance of GPT-4 Turbo is the most stable across various tasks. Furthermore, our analysis of these LLMs, which are extended to handle long-context scenarios, indicates that there is potential for improvement as the length of the input context and the intricacy of the tasks are increasing.

Related papers

• CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels [11.614599448394374]
  CNNSum is a benchmark for long-context summarization based on Chinese novels. It features four subsets totaling 695 samples, with lengths ranging from 16k to 128k. We conduct experiments to explore and improve long-context summarization.
  arXiv  Detail & Related papers  (2024-12-03T20:35:57Z)
• LongIns: A Challenging Long-context Instruction-based Exam for LLMs [44.51209510772957]
  Long-context capabilities of large language models (LLMs) have been a hot topic in recent years. We propose the LongIns benchmark dataset, a challenging long-context instruction-based exam for LLMs.
  arXiv  Detail & Related papers  (2024-06-25T14:31:26Z)
• Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA [71.04146366608904]
  Long-context modeling capabilities have garnered widespread attention, leading to the emergence of Large Language Models (LLMs) with ultra-context windows. We propose a novel long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) Loong introduces four types of tasks with a range of context lengths: Spotlight Locating, Comparison, Clustering, and Chain of Reasoning.
  arXiv  Detail & Related papers  (2024-06-25T09:42:56Z)
• Long Context Alignment with Short Instructions and Synthesized Positions [56.1267385315404]
  This paper introduces Step-Skipping Alignment (SkipAlign) It is a new technique designed to enhance the long-context capabilities of Large Language Models (LLMs) With a careful selection of the base model and alignment datasets, SkipAlign with only 6B parameters achieves it's best performance and comparable with strong baselines like GPT-3.5-Turbo-16K on LongBench.
  arXiv  Detail & Related papers  (2024-05-07T01:56:22Z)
• MileBench: Benchmarking MLLMs in Long Context [31.211260223575092]
  We introduce MileBench, a benchmark designed to test the MultImodal Long-contExt capabilities of MLLMs. We systematically assess MLLMs' long-context adaptation capacity and their ability to complete tasks in long-context scenarios. Results show that while the closed-source GPT-4o outperforms others, most open-source MLLMs struggle in long-context situations.
  arXiv  Detail & Related papers  (2024-04-29T09:19:05Z)
• Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks [76.43527940649939]
  We introduce Ada-LEval, a benchmark for evaluating the long-context understanding of large language models (LLMs) Ada-LEval includes two challenging subsets, TSort and BestAnswer, which enable a more reliable evaluation of LLMs' long context capabilities. We evaluate 4 state-of-the-art closed-source API models and 6 open-source models with Ada-LEval.
  arXiv  Detail & Related papers  (2024-04-09T17:30:48Z)
• XL$^2$Bench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies [45.31042312867939]
  Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks but are constrained by their small context window sizes. Various efforts have been proposed to expand the context window to accommodate even up to 200K input tokens. We introduce a benchmark for extremely long context understanding with long-range dependencies, XL$2$Bench.
  arXiv  Detail & Related papers  (2024-04-08T12:29:07Z)
• M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models [58.54538318912159]
  M4LE is a benchmark for evaluating the long-sequence capability of large language models (LLMs) M4LE is based on a diverse NLP task pool comprising 36 NLP task types and 12 domains. We conducted a systematic evaluation on 11 well-established LLMs, especially those optimized for long-sequence inputs.
  arXiv  Detail & Related papers  (2023-10-30T03:11:30Z)
• BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models [141.21603469555225]
  Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. We propose BAMBOO, a multi-task long context benchmark. It consists of 10 datasets from 5 different long text understanding tasks.
  arXiv  Detail & Related papers  (2023-09-23T11:36:15Z)
• L-Eval: Instituting Standardized Evaluation for Long Context Language Models [91.05820785008527]
  We propose L-Eval to institute a more standardized evaluation for long context language models (LCLMs) We build a new evaluation suite containing 20 sub-tasks, 508 long documents, and over 2,000 human-labeled query-response pairs. Results show that popular n-gram matching metrics generally can not correlate well with human judgment.
  arXiv  Detail & Related papers  (2023-07-20T17:59:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.