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Found 33 papers, 16 papers with code
ReZero is All You Need: Fast Convergence at Large Depth
Deep networks often suffer from vanishing or exploding gradients due to inefficient signal propagation, leading to long training times or convergence difficulties.
Self-Refine: Iterative Refinement with Self-Feedback
Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.
Generating Personalized Recipes from Historical User Preferences
Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes.
Ranked #1 on
Recipe Generation
on Food.com
Large Language Models as Zero-Shot Conversational Recommenders
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions.
Improving Neural Story Generation by Targeted Common Sense Grounding
Stories generated with neural language models have shown promise in grammatical and stylistic consistency.
Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena
Recent advancements in Large Language Models (LLMs) showcase advanced reasoning, yet NLP evaluations often depend on static benchmarks.
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.
Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge
The ability to generate clarification questions i. e., questions that identify useful missing information in a given context, is important in reducing ambiguity.
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.
Fault Detection Engine in Intelligent Predictive Analytics Platform for DCIM
With the advancement of huge data generation and data handling capability, Machine Learning and Probabilistic modelling enables an immense opportunity to employ predictive analytics platform in high security critical industries namely data centers, electricity grids, utilities, airport etc.
Kernelized Weighted SUSAN based Fuzzy C-Means Clustering for Noisy Image Segmentation
The paper proposes a novel Kernelized image segmentation scheme for noisy images that utilizes the concept of Smallest Univalue Segment Assimilating Nucleus (SUSAN) and incorporates spatial constraints by computing circular colour map induced weights.
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.
Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit
We propose a post-OCR text correction approach for digitising texts in Romanised Sanskrit.
Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce
Extracting accurate attribute qualities from product titles is a vital component in delivering eCommerce customers with a rewarding online shopping experience via an enriched faceted search.
Interview: A Large-Scale Open-Source Corpus of Media Dialog
Compared to existing large-scale proxies for conversational data, language models trained on our dataset exhibit better zero-shot out-of-domain performance on existing spoken dialog datasets, demonstrating its usefulness in modeling real-world conversations.
Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding
In this work, we perform the first large-scale analysis of discourse in media dialog and its impact on generative modeling of dialog turns, with a focus on interrogative patterns and use of external knowledge.
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
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations
Our framework improves over previous methods by: (i) reaching SOTA task performance while also providing explanations, (ii) providing two types of explanations, while existing models usually provide only one type, and (iii) beating by a large margin the previous SOTA in terms of quality of both types of explanations.
Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decoding
Written language carries explicit and implicit biases that can distract from meaningful signals.
Self-Supervised Bot Play for Conversational Recommendation with Justifications
Conversational recommender systems offer the promise of interactive, engaging ways for users to find items they enjoy.
On Faithfulness and Coherence of Language Explanations for Recommendation Systems
Reviews contain rich information about product characteristics and user interests and thus are commonly used to boost recommender system performance.
Factual and Informative Review Generation for Explainable Recommendation
Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings.
Controlling Bias Exposure for Fair Interpretable Predictions
However, when sensitive information is semantically entangled with the task information of the input, e. g., gender information is predictive for a profession, a fair trade-off between task performance and bias mitigation is difficult to achieve.
InterFair: Debiasing with Natural Language Feedback for Fair Interpretable Predictions
In the other setup, human feedback was able to disentangle associated bias and predictive information from the input leading to superior bias mitigation and improved task performance (4-5%) simultaneously.
Aligning Language Models to User Opinions
An important aspect of developing LLMs that interact with humans is to align models' behavior to their users.
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations
While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited research in detecting and alleviating inconsistencies among generated NLEs.
CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
Language agents have shown some ability to interact with an external environment, e. g., a virtual world such as ScienceWorld, to perform complex tasks, e. g., growing a plant, without the startup costs of reinforcement learning.
To Tell The Truth: Language of Deception and Language Models
Text-based misinformation permeates online discourses, yet evidence of people's ability to discern truth from such deceptive textual content is scarce.
One Size Does Not Fit All: Customizing Open-Domain Procedures
Our approach is to test several simple multi-LLM-agent architectures for customization, as well as an end-to-end LLM, using a new evaluation set, called CustomPlans, of over 200 WikiHow procedures each with a customization need.
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
We evaluate our method in the classic videogame NetHack and the text environment ScienceWorld to demonstrate SSO's ability to optimize a set of skills and perform in-context policy improvement.
Data-driven Discovery with Large Generative Models
With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially.
Tell, Don't Show!: Language Guidance Eases Transfer Across Domains in Images and Videos
We introduce LaGTran, a novel framework that utilizes readily available or easily acquired text descriptions to guide robust transfer of discriminative knowledge from labeled source to unlabeled target data with domain shifts.
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing.
