Sentence Ordering
1개 벤치마크 · 논문 51편 · 이 태스크의 논문 보기 →
Benchmarks
EconLogicQA
Most implemented
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
Topological Sort for Sentence Ordering
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
OrderSum: Semantic Sentence Ordering for Extractive Summarization
EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning
Are Large Language Models Temporally Grounded?
Papers
ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts
Summarizing deeply nested discussion threads requires handling interleaved replies, quotes, and overlapping topics, which standard LLM summarizers struggle to capture reliably. We introduce ThreadSumm, a multi-stage LLM …
Sentence OrderingBurn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences?
This paper introduces the TempVS benchmark, which focuses on temporal grounding and reasoning capabilities of Multimodal Large Language Models (MLLMs) in image sequences. TempVS consists of three main tests (i.e., event …
SentenceSentence OrderingOrderSum: Semantic Sentence Ordering for Extractive Summarization
There are two main approaches to recent extractive summarization: the sentence-level framework, which selects sentences to include in a summary individually, and the summary-level framework, which generates multiple cand…
Extractive SummarizationSentenceSentence OrderingEconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning
In this paper, we introduce EconLogicQA, a rigorous benchmark designed to assess the sequential reasoning capabilities of large language models (LLMs) within the intricate realms of economics, business, and supply chain …
ArticlesManagementMultiple Choice Question Answering (MCQA)+2Are Large Language Models Temporally Grounded?
Are Large language models (LLMs) temporally grounded? Since LLMs cannot perceive and interact with the environment, it is impossible to answer this question directly. Instead, we provide LLMs with textual narratives and …
Common Sense ReasoningIn-Context LearningSentenceSentence OrderingNon-Autoregressive Sentence Ordering
Existing sentence ordering approaches generally employ encoder-decoder frameworks with the pointer net to recover the coherence by recurrently predicting each sentence step-by-step. Such an autoregressive manner only lev…
DecoderSentenceSentence Ordering