306 citations · 1.3k across the 118 of their papers we have counts for
33 papers · 1 filter
Contrastive Chain-of-Thought Prompting
Yew Ken Chia, Guizhen Chen, Luu Anh Tuan +2
Despite the success of chain of thought in enhancing language model reasoning, the underlying process remains less well understood. Although logically sound reasoning appears inher…
Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning
Qingyu Tan, Hwee Tou Ng, Lidong Bing
Knowledge in the real world is being updated constantly. However, it is costly to frequently update large language models (LLMs). Therefore, it is crucial for LLMs to understand th…
Exploring the Potential of Large Language Models in Computational Argumentation
Guizhen Chen, Liying Cheng, Luu Anh Tuan +1
Computational argumentation has become an essential tool in various domains, including law, public policy, and artificial intelligence. It is an emerging research field in natural…
SOUL: Towards Sentiment and Opinion Understanding of Language
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan +1
Sentiment analysis is a well-established natural language processing task, with sentiment polarity classification being one of its most popular and representative tasks. However, d…
Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning
Huiming Wang, Zhaodonghui Li, Liying Cheng +2
Recently, large language models (LLMs) have emerged as a groundbreaking technology and their unparalleled text generation capabilities have sparked interest in their application to…
Multilingual Jailbreak Challenges in Large Language Models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan +1
While large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, they pose potential safety concerns, such as the ``jailbreak'' problem, wherein mal…
Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models
Qingyu Tan, Hwee Tou Ng, Lidong Bing
Reasoning about time is of fundamental importance. Many facts are time-dependent. For example, athletes change teams from time to time, and different government officials are elect…
Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data
Qingyu Tan, Lu Xu, Lidong Bing +1
Relation extraction (RE) aims to extract relations from sentences and documents. Existing relation extraction models typically rely on supervised machine learning. However, recent…
INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
Yew Ken Chia, Pengfei Hong, Lidong Bing +1
Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents. These models,…
Reasoning Implicit Sentiment with Chain-of-Thought Prompting
Hao Fei, Bobo Li, Qian Liu +3
While sentiment analysis systems try to determine the sentiment polarities of given targets based on the key opinion expressions in input texts, in implicit sentiment analysis (ISA…
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning
Ran Zhou, Xin Li, Lidong Bing +2
In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data. However, due to sub…
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts
Xuan-Phi Nguyen, Sharifah Mahani Aljunied, Shafiq Joty +1
Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. However, in low-resource languages, obtaining such hand-picked exemplars can…
M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
Wenxuan Zhang, Sharifah Mahani Aljunied, Chang Gao +2
Despite the existence of various benchmarks for evaluating natural language processing models, we argue that human exams are a more suitable means of evaluating general intelligenc…
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
Hang Zhang, Xin Li, Lidong Bing
We present Video-LLaMA a multi-modal framework that empowers Large Language Models (LLMs) with the capability of understanding both visual and auditory content in the video. Video-…
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach
Jia Guo, Liying Cheng, Wenxuan Zhang +3
Argument mining involves multiple sub-tasks that automatically identify argumentative elements, such as claim detection, evidence extraction, stance classification, etc. However, e…
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling
Shengqiong Wu, Hao Fei, Yixin Cao +2
Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. To co…
Zero-Shot Text Classification via Self-Supervised Tuning
Chaoqun Liu, Wenxuan Zhang, Guizhen Chen +4
Existing solutions to zero-shot text classification either conduct prompting with pre-trained language models, which is sensitive to the choices of templates, or rely on large-scal…
Sentiment Analysis in the Era of Large Language Models: A Reality Check
Wenxuan Zhang, Yue Deng, Bing Liu +2
Sentiment analysis (SA) has been a long-standing research area in natural language processing. It can offer rich insights into human sentiments and opinions and has thus seen consi…
mPMR: A Multilingual Pre-trained Machine Reader at Scale
Weiwen Xu, Xin Li, Wai Lam +1
We present multilingual Pre-trained Machine Reader (mPMR), a novel method for multilingual machine reading comprehension (MRC)-style pre-training. mPMR aims to guide multilingual p…
Better Sampling of Negatives for Distantly Supervised Named Entity Recognition
Lu Xu, Lidong Bing, Wei Lu
Distantly supervised named entity recognition (DS-NER) has been proposed to exploit the automatically labeled training data instead of human annotations. The distantly annotated da…
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation
Huiming Wang, Liying Cheng, Wenxuan Zhang +2
Recently, data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recogniti…
Easy-to-Hard Learning for Information Extraction
Chang Gao, Wenxuan Zhang, Wai Lam +1
Information extraction (IE) systems aim to automatically extract structured information, such as named entities, relations between entities, and events, from unstructured texts. Wh…
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan +1
Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain.…
Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
Ruochen Zhao, Xingxuan Li, Shafiq Joty +2
As large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of fa…