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20102023
most citedSiren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

244 citations · 1.5k across the 71 of their papers we have counts for

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Showing cs.CLShow all

19 papers · 1 filter

cs.CL2023

TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild

Huayang Li, Siheng Li, Deng Cai +5

Large language models with instruction-following abilities have revolutionized the field of artificial intelligence. These models show exceptional generalizability to tackle variou…

cs.CL2023★ 244 cited

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Yue Zhang, Yafu Li, Leyang Cui +13

While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit h…

cs.CL2022★ 4 cited

Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters

Nuo Chen, Yan Wang, Haiyun Jiang +5

In recent years, Dialogue-style Large Language Models (LLMs) such as ChatGPT and GPT4 have demonstrated immense potential in constructing open-domain dialogue agents. However, alig…

cs.CL2021★ 3 cited

Assessing Dialogue Systems with Distribution Distances

Jiannan Xiang, Yahui Liu, Deng Cai +3

An important aspect of developing dialogue systems is how to evaluate and compare the performance of different systems. Existing automatic evaluation metrics are based on turn-leve…

cs.CL2021★ 5 cited

Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering

Weiwen Xu, Huihui Zhang, Deng Cai +1

Knowledge retrieval and reasoning are two key stages in multi-hop question answering (QA) at web scale. Existing approaches suffer from low confidence when retrieving evidence fact…

cs.CL2021★ 10 cited

Non-Autoregressive Text Generation with Pre-trained Language Models

Yixuan Su, Deng Cai, Yan Wang +4

Non-autoregressive generation (NAG) has recently attracted great attention due to its fast inference speed. However, the generation quality of existing NAG models still lags behind…

cs.CL2020★ 3 cited

Narrative Incoherence Detection

Deng Cai, Yizhe Zhang, Yichen Huang +2

We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding: Given a multi-sentence narrative, decide whether there exist any…

cs.CL2020★ 2 cited

EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications

Minghui Qiu, Peng Li, Chengyu Wang +8

The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP)…

cs.CL2020

Adversarial Mutual Information for Text Generation

Boyuan Pan, Yazheng Yang, Kaizhao Liang +5

Recent advances in maximizing mutual information (MI) between the source and target have demonstrated its effectiveness in text generation. However, previous works paid little atte…

cs.CL2020★ 7 cited

Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy

Yixuan Su, Deng Cai, Yan Wang +4

Stylistic response generation is crucial for building an engaging dialogue system for industrial use. While it has attracted much research interest, existing methods often generate…

cs.CL2020★ 5 cited

The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection

Zibo Lin, Deng Cai, Yan Wang +3

Response selection plays a vital role in building retrieval-based conversation systems. Despite that response selection is naturally a learning-to-rank problem, most prior works ta…

cs.CL2020

Bi-Decoder Augmented Network for Neural Machine Translation

Boyuan Pan, Yazheng Yang, Zhou Zhao +2

Neural Machine Translation (NMT) has become a popular technology in recent years, and the encoder-decoder framework is the mainstream among all the methods. It's obvious that the q…

cs.CL2019★ 3 cited

Reinforced Dynamic Reasoning for Conversational Question Generation

Boyuan Pan, Hao Li, Ziyu Yao +2

This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i.e., previous turn…

cs.CL2019★ 17 cited

MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models

Boyuan Pan, Yazheng Yang, Hao Li +4

Machine Comprehension (MC) is one of the core problems in natural language processing, requiring both understanding of the natural language and knowledge about the world. Rapid pro…

cs.CL2019★ 1 cited

Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference

Boyuan Pan, Yazheng Yang, Zhou Zhao +3

Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing. It requires to infer the…

cs.CL2018★ 15 cited

Dial2Desc: End-to-end Dialogue Description Generation

Haojie Pan, Junpei Zhou, Zhou Zhao +3

We first propose a new task named Dialogue Description (Dial2Desc). Unlike other existing dialogue summarization tasks such as meeting summarization, we do not maintain the natural…

cs.CL2017★ 10 cited

Dialogue Act Recognition via CRF-Attentive Structured Network

Zheqian Chen, Rongqin Yang, Zhou Zhao +2

Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Cur…

cs.CL2017★ 2 cited

Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation

Boyuan Pan, Hao Li, Zhou Zhao +2

In this paper, we consider the problem of machine reading task when the questions are in the form of keywords, rather than natural language. In recent years, researchers have achie…

cs.CL2017★ 15 cited

Smarnet: Teaching Machines to Read and Comprehend Like Human

Zheqian Chen, Rongqin Yang, Bin Cao +3

Machine Comprehension (MC) is a challenging task in Natural Language Processing field, which aims to guide the machine to comprehend a passage and answer the given question. Many e…