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Works334 from public data
- A Survey on Evaluation of Large Language Models3,896
This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate, and offers invaluable insights to researchers in the realm of LLMs evaluation.
- 🧜Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models1,188
This paper presents taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyzes existing approaches aiming at mitigating LLm hallucination, and discusses potential directions for future research.
- Template-Based Named Entity Recognition Using BART407
A template-based method for NER is proposed, treating NER as a language model ranking problem in a sequence-to-sequence framework, where original sentences and statement templates filled by candidate named entity span are regarded as the source sequence and the target sequence, respectively.
- Transition-based Dependency Parsing with Rich Non-local Features374
This paper shows that it can improve the accuracy of transition-based dependency parsers by considering even richer feature sets than those employed in previous systems by improving the accuracy in the standard Penn Treebank setup and rivaling the best results overall.
- PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts351
This study uses a plethora of adversarial textual attacks targeting prompts across multiple levels: character, word, sentence, and semantic to demonstrate that contemporary LLMs are not robust to adversarial prompts.
- DialogSum: A Real-Life Scenario Dialogue Summarization Dataset338
Experimental results show unique challenges in dialogue summarization, such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense, which require specific representation learning technologies to better deal with.
- A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing338
A beam-search-based parser that combines both graph-based and transition-based parsing into a single system for training and decoding is proposed, showing that it outperforms both the pure graph- based and the pure transition- based parsers.
- Gated Neural Networks for Targeted Sentiment Analysis311
A sentence-level neural model is proposed to address the limitation of pooling functions, which do not explicitly model tweet-level semantics and gives significantly higher accuracies compared to the current best method for targeted sentiment analysis.
- A Graph-to-Sequence Model for AMR-to-Text Generation269
This work introduces a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics, and shows superior results to existing methods in the literature.
- Neural Networks for Open Domain Targeted Sentiment247
This work empirically studies the effect of word embeddings and automatic feature combinations on the open domain targeted sentiment task by extending a CRF baseline using neural networks, and proposes a novel integration of neural and discrete features which combines their relative advantages, leading to significantly higher results compared to both baselines.
- Sentence-State LSTM for Text Representation226
This work investigates an alternative LSTM structure for encoding text, which consists of a parallel state for each word, and shows that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers.
- Syntactic Processing Using the Generalized Perceptron and Beam Search221
It is argued that the conceptual and computational simplicity of the framework, together with its language-independent nature, make it a competitive choice for a range of syntactic processing tasks and one that should be considered for comparison by developers of alternative approaches.
- Tweet Sarcasm Detection Using Deep Neural Network217
This work investigates the use of neural network for tweet sarcasm detection, and compares the effects of the continuous automatic features with discrete manual features.
- Automatic Features for Essay Scoring – An Empirical Study212
This paper employs convolutional neural network for the effect of automatically learning features, and compares the result with the state-of-art discrete baselines.
- Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter204
This paper proposes Lexicon Enhanced BERT (LEBERT) for Chinese sequence labeling, which integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer, and facilitates deep lexiconknowledge fusion at the lower layers of BERT.
- N-ary Relation Extraction using Graph-State LSTM201
This work proposes a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing, and speeds up computation by allowing more parallelization.
- Evaluating Commonsense in Pre-Trained Language Models200
This work studies the commonsense ability of GPT, BERT, XLNet, and RoBERTa by testing them on seven challenging benchmarks, finding that language modeling and its variants are effective objectives for promoting models' commonsens ability while bi-directional context and larger training set are bonuses.
- MuTual: A Dataset for Multi-Turn Dialogue Reasoning182
MuTual is introduced, a novel dataset for Multi-Turn dialogue Reasoning, consisting of 8,860 manually annotated dialogues based on Chinese student English listening comprehension exams, which shows that there is ample room for improving reasoning ability.
- CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP179
A data augmentation framework to generate multi-lingual code-switching data to fine-tune mBERT, which encourages model to align representations from source and multiple target languages once by mixing their context information.
- The INTERSPEECH 2017 Computational Paralinguistics Challenge: Addressee, Cold & Snoring174
These sub-challenges, their conditions, and the baseline feature extraction and classifiers are described, which include data-learnt feature representations by end-to-end learning with convolutional and recurrent neural networks, and bag-of-audiowords for the first time in the challenge series.
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- Leveraging Context Information for Natural Question Generation162
This work proposes a model that matches the answer with the passage before generating the question and shows that this model outperforms the existing state of the art using rich features.
- Context-Sensitive Twitter Sentiment Classification Using Neural Network151
A context-based neural network model for Twitter sentiment analysis is proposed, incorporating contextualized features from relevant Tweets into the model in the form of word embedding vectors.
- Joint Word Segmentation and POS Tagging Using a Single Perceptron144
This paper proposes a joint segmentation and POS tagging model that does not impose any hard constraints on the interaction between word and POS information, and uses a discriminative statistical model, trained using the generalized perceptron algorithm.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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