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Found 15 papers, 5 papers with code
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
GenSERP: Large Language Models for Whole Page Presentation
(2) An answer generation phase where the LLM populates the layouts with the retrieved content.
DUBLIN -- Document Understanding By Language-Image Network
Visual document understanding is a complex task that involves analyzing both the text and the visual elements in document images.
Ranked #1 on
Visual Question Answering (VQA)
on DeepForm
METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals
We present an efficient method of pretraining large-scale autoencoding language models using training signals generated by an auxiliary model.
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
We present a new framework AMOS that pretrains text encoders with an Adversarial learning curriculum via a Mixture Of Signals from multiple auxiliary generators.
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
Next, we detail the training process, the design of our training corpus, and our data curation techniques, which we believe is a key ingredient to the success of the model.
Ranked #33 on
Sentence Completion
on HellaSwag
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
The first token-level task, Corrective Language Modeling, is to detect and correct tokens replaced by the auxiliary model, in order to better capture token-level semantics.
Pretrain Knowledge-Aware Language Models
Rather, we simply signal the existence of entities to the input of the transformer in pretraining, with an entity-extended tokenizer; and at the output, with an additional entity prediction task.
Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention
Transformers have achieved new heights modeling natural language as a sequence of text tokens.
Ranked #42 on
Question Answering
on HotpotQA
Generic Intent Representation in Web Search
This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search.
An Axiomatic Approach to Regularizing Neural Ranking Models
The training of these models involve a search for appropriate parameter values based on large quantities of labeled examples.
Towards Language Agnostic Universal Representations
When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in, even if the math lessons were only taught in one language.
Optimizing Query Evaluations using Reinforcement Learning for Web Search
In web search, typically a candidate generation step selects a small set of documents---from collections containing as many as billions of web pages---that are subsequently ranked and pruned before being presented to the user.
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.
