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Found 38 papers, 10 papers with code
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages
A subsequent evaluation on multilingual treebanks shows that the model with subword information achieves state-of-the-art results on many languages, further supporting a distributional model of syntactic acquisition.
Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition
Abstract This article describes a simple PCFG induction model with a fixed category domain that predicts a large majority of attested constituent boundaries, and predicts labels consistent with nearly half of attested constituent labels on a standard evaluation data set of child-directed speech.
Instance-adaptive training with noise-robust losses against noisy labels
In order to alleviate the huge demand for annotated datasets for different tasks, many recent natural language processing datasets have adopted automated pipelines for fast-tracking usable data.
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Despite the impressive capabilities of Large Language Models (LLMs) on various tasks, they still struggle with scenarios that involves complex reasoning and planning.
Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models
Current state-of-the-art approaches refine decoding by contrasting early-exit distributions from a lower layer with the final layer to exploit information related to factuality within the model forward procedure.
Self-Consistency Boosts Calibration for Math Reasoning
Calibration, which establishes the correlation between accuracy and model confidence, is important for LLM development.
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation
In this paper, we present a high-quality benchmark named multi-source Wizard of Wikipedia (Ms. WoW) for evaluating multi-source dialogue knowledge selection and response generation.
Collaborative decoding of critical tokens for boosting factuality of large language models
The most common training pipeline for large language models includes pretraining, finetuning and aligning phases, with their respective resulting models, such as the pretrained model and the finetuned model.
Fine-Grained Self-Endorsement Improves Factuality and Reasoning
Experiments on Biographies show that our method can effectively improve the factuality of generations with simple and intuitive prompts across different scales of LLMs.
Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation
Despite showing increasingly human-like abilities, large language models (LLMs) often struggle with factual inaccuracies, i. e. "hallucinations", even when they hold relevant knowledge.
Inconsistent dialogue responses and how to recover from them
One critical issue for chat systems is to stay consistent about preferences, opinions, beliefs and facts of itself, which has been shown a difficult problem.
TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs
We construct a hierarchical task tree encompassing 7 major areas covering over 200 categories and over 800 tasks, which covers diverse capabilities such as question answering, reasoning, multiturn dialogue, and text generation, to evaluate LLMs in a comprehensive and in-depth manner.
The Trickle-down Impact of Reward (In-)consistency on RLHF
We propose Contrast Instructions -- a benchmarking strategy for the consistency of RM.
Stabilizing RLHF through Advantage Model and Selective Rehearsal
Large Language Models (LLMs) have revolutionized natural language processing, yet aligning these models with human values and preferences using RLHF remains a significant challenge.
Friend-training: Learning from Models of Different but Related Tasks
Current self-training methods such as standard self-training, co-training, tri-training, and others often focus on improving model performance on a single task, utilizing differences in input features, model architectures, and training processes.
Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing
Pretrained natural language processing (NLP) models have achieved high overall performance, but they still make systematic errors.
Salience Allocation as Guidance for Abstractive Summarization
Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.
Ranked #7 on
Abstractive Text Summarization
on CNN / Daily Mail
Learning a Grammar Inducer from Massive Uncurated Instructional Videos
While previous work focuses on building systems for inducing grammars on text that are well-aligned with video content, we investigate the scenario, in which text and video are only in loose correspondence.
Hierarchical Context Tagging for Utterance Rewriting
HCT (i) tags the source string with token-level edit actions and slotted rules and (ii) fills in the resulting rule slots with spans from the dialogue context.
Distant finetuning with discourse relations for stance classification
Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with individual debate topics.
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories
We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks.
Domain-Adaptive Pretraining Methods for Dialogue Understanding
Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks.
Video-aided Unsupervised Grammar Induction
We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video.
Grounded PCFG Induction with Images
Recent work in unsupervised parsing has tried to incorporate visual information into learning, but results suggest that these models need linguistic bias to compete against models that only rely on text.
Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction
Syntactic surprisal has been shown to have an effect on human sentence processing, and can be predicted from prefix probabilities of generative incremental parsers.
The Importance of Category Labels in Grammar Induction with Child-directed Utterances
Recent progress in grammar induction has shown that grammar induction is possible without explicit assumptions of language-specific knowledge.
Unsupervised Learning of PCFGs with Normalizing Flow
This paper describes a neural PCFG inducer which employs context embeddings (Peters et al., 2018) in a normalizing flow model (Dinh et al., 2015) to extend PCFG induction to use semantic and morphological information.
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction
In unsupervised grammar induction, data likelihood is known to be only weakly correlated with parsing accuracy, especially at convergence after multiple runs.
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction
There have been several recent attempts to improve the accuracy of grammar induction systems by bounding the recursive complexity of the induction model (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016; Jin et al., 2018).
Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System
When interpreting questions in a virtual patient dialogue system one must inevitably tackle the challenge of a long tail of relatively infrequently asked questions.
Unsupervised Grammar Induction with Depth-bounded PCFG
There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016).
Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System
For medical students, virtual patient dialogue systems can provide useful training opportunities without the cost of employing actors to portray standardized patients.
Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input
This paper presents a new memory-bounded left-corner parsing model for unsupervised raw-text syntax induction, using unsupervised hierarchical hidden Markov models (UHHMM).
