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On Context Utilization in Summarization with Large Language Models
Mathieu Ravaut, Aixin Sun, Nancy F. Chen +1
Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle lon…
Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution
Xinze Li, Yixin Cao, Liangming Pan +2
Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations. Although language attribution can be a potential solution, there are n…
FLM-101B: An Open LLM and How to Train It with $100K Budget
Xiang Li, Yiqun Yao, Xin Jiang +10
Large language models (LLMs) are considered important approaches towards foundational machine intelligence, achieving remarkable success in Natural Language Processing and multimod…
Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting
Chen Chen, Yufei Wang, Aixin Sun +2
Knowledge Graph Completion (KGC) often requires both KG structural and textual information to be effective. Pre-trained Language Models (PLMs) have been used to learn the textual i…
Few-shot Event Detection: An Empirical Study and a Unified View
Yubo Ma, Zehao Wang, Yixin Cao +1
Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the unde…
Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation
Xinze Li, Yixin Cao, Muhao Chen +1
Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of co…
Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection
Kuicai Dong, Aixin Sun, Jung-Jae Kim +1
Open Information Extraction (OIE) aims to extract factual relational tuples from open-domain sentences. Downstream tasks use the extracted OIE tuples as facts, without examining th…
Open Information Extraction via Chunks
Kuicai Dong, Aixin Sun, Jung-Jae Kim +1
Open Information Extraction (OIE) aims to extract relational tuples from open-domain sentences. Existing OIE systems split a sentence into tokens and recognize token spans as tuple…
FreeLM: Fine-Tuning-Free Language Model
Xiang Li, Xin Jiang, Xuying Meng +2
Pre-trained language models (PLMs) have achieved remarkable success in NLP tasks. Despite the great success, mainstream solutions largely follow the pre-training then finetuning pa…
GCRE-GPT: A Generative Model for Comparative Relation Extraction
Yequan Wang, Hengran Zhang, Aixin Sun +1
Given comparative text, comparative relation extraction aims to extract two targets (\eg two cameras) in comparison and the aspect they are compared for (\eg image quality). The ex…
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!
Yubo Ma, Yixin Cao, YongChing Hong +1
Large Language Models (LLMs) have made remarkable strides in various tasks. Whether LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains an…