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Found 31 papers, 23 papers with code
Truth-Conditional Captions for Time Series Data
In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week.
Formulating Neural Sentence Ordering as the Asymmetric Traveling Salesman Problem
However, such an approach has major limitations – it cannot handle the presence of cycles in the resulting graphs and considers only the binary presence/absence of edges rather than a more granular score.
Towards Robust Evaluation of Unlearning in LLMs via Data Transformations
Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents.
Steering Large Language Models between Code Execution and Textual Reasoning
Textual reasoning has inherent limitations in solving tasks with challenges in math, logics, optimization, and searching, which is unlikely to be solved by simply scaling up the model and data size.
Learning to Retrieve Iteratively for In-Context Learning
We introduce iterative retrieval, a novel framework that empowers retrievers to make iterative decisions through policy optimization.
Interpreting User Requests in the Context of Natural Language Standing Instructions
Users of natural language interfaces, generally powered by Large Language Models (LLMs), often must repeat their preferences each time they make a similar request.
SCREWS: A Modular Framework for Reasoning with Revisions
To enable exploration in this space, we present SCREWS, a modular framework for reasoning with revisions.
Natural Language Decomposition and Interpretation of Complex Utterances
Designing natural language interfaces has historically required collecting supervised data to translate user requests into carefully designed intent representations.
Ontologically Faithful Generation of Non-Player Character Dialogues
We introduce a language generation task grounded in a popular video game environment.
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation
To this end, we propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation.
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding
Satisfying these constraints simultaneously is difficult for the two predominant paradigms in language generation: neural language modeling and rule-based generation.
Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation
Target-guided response generation enables dialogue systems to smoothly transition a conversation from a dialogue context toward a target sentence.
Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection
In this paper, we propose a post-hoc knowledge-injection technique where we first retrieve a diverse set of relevant knowledge snippets conditioned on both the dialog history and an initial response from an existing dialog model.
Truth-Conditional Captioning of Time Series Data
In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week.
Investigating Robustness of Dialog Models to Popular Figurative Language Constructs
Humans often employ figurative language use in communication, including during interactions with dialog systems.
Unsupervised Enrichment of Persona-grounded Dialog with Background Stories
Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich.
Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation
Multiple different responses are often plausible for a given open domain dialog context.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions
Existing persona-grounded dialog models often fail to capture simple implications of given persona descriptions, something which humans are able to do seamlessly.
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering
The third dataset eOBQA is constructed by adding explanation annotations to the OBQA dataset to test generalization of models trained on eQASC.
Ranked #1 on
Reasoning Chain Explanations
on eQASC
Learning Rhyming Constraints using Structured Adversaries
Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry.
A Sociolinguistic Study of Online Echo Chambers on Twitter
Online social media platforms such as Facebook and Twitter are increasingly facing criticism for polarization of users.
Learning to Describe Differences Between Pairs of Similar Images
We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences.
Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data
This paper examines the problem of generating natural language descriptions of chess games.
SPINE: SParse Interpretable Neural Embeddings
We propose a novel variant of denoising k-sparse autoencoders that generates highly efficient and interpretable distributed word representations (word embeddings), beginning with existing word representations from state-of-the-art methods like GloVe and word2vec.
Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models
Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose.
CharManteau: Character Embedding Models For Portmanteau Creation
Portmanteaus are a word formation phenomenon where two words are combined to form a new word.
Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models
Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose.
Generating Appealing Brand Names
Providing appealing brand names to newly launched products, newly formed companies or for renaming existing companies is highly important as it can play a crucial role in deciding its success or failure.
