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Found 18 papers, 14 papers with code
An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models
Backdoor attacks present significant threats to Large Language Models (LLMs), particularly with the rise of third-party services that offer API integration and prompt engineering.
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Addressing the challenge of effectively processing long contexts has become a critical issue for Large Language Models (LLMs).
When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
Our critical survey based on the newly categorized research questions shows that (1) no prior work demonstrates successful self-correction with feedback from prompted LLMs in general tasks, (2) self-correction works well in tasks that can use reliable external feedback, and (3) large-scale fine-tuning enables self-correction.
Evaluating LLMs at Detecting Errors in LLM Responses
This work introduces ReaLMistake, the first error detection benchmark consisting of objective, realistic, and diverse errors made by LLMs.
A General Benchmark Framework is Dynamic Graph Neural Network Need
In conclusion, this paper identifies the lack of a standardized benchmark framework as a current limitation in dynamic graph learning research .
Fair Abstractive Summarization of Diverse Perspectives
However, current work in summarization metrics and Large Language Models (LLMs) evaluation has not explored fair abstractive summarization.
FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
In this paper, we investigate and improve faithfulness in summarization on a broad range of medical summarization tasks.
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
However, existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications, impeding a comprehensive and unified evaluation of CLSP on a diverse range of NLs and MRs. To this end, we present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations by examining and selecting 9 existing datasets to cover 5 tasks and 164 domains.
MACSum: Controllable Summarization with Mixed Attributes
We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning.
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications
To automatically correct handwritten assignments, the traditional approach is to use an OCR model to recognize characters and compare them to answers.
Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
To the best of our knowledge, Summ$^N$ is the first multi-stage split-then-summarize framework for long input summarization.
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Transformer-based models have achieved state-of-the-art performance on short-input summarization.
An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.
SummerTime: Text Summarization Toolkit for Non-experts
We also provide explanations for models and evaluation metrics to help users understand the model behaviors and select models that best suit their needs.
Logic-Consistency Text Generation from Semantic Parses
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries.
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL
Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any input.
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition
The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), e. g., long-short-term-memory (LSTM).
Ranked #24 on
Named Entity Recognition (NER)
on Ontonotes v5 (English)
