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Found 20 papers, 9 papers with code
BAGEL: Bootstrapping Agents by Guiding Exploration with Language
Unfortunately, LM agents often fail to generalize to new environments without human demonstrations.
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking
However, even pretrain reward ensembles do not eliminate reward hacking: we show several qualitative reward hacking phenomena that are not mitigated by ensembling because all reward models in the ensemble exhibit similar error patterns.
From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
Much of the previous work towards digital agents for graphical user interfaces (GUIs) has relied on text-based representations (derived from HTML or other structured data sources), which are not always readily available.
QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations
To study the ability of retrieval systems to meet such information needs, we construct QUEST, a dataset of 3357 natural language queries with implicit set operations, that map to a set of entities corresponding to Wikipedia documents.
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
Visually-situated language is ubiquitous -- sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms.
Ranked #17 on
Visual Question Answering (VQA)
on InfographicVQA
Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing
Then, it retrieves exemplars with outputs similar to the preliminary prediction which are used to generate a final prediction.
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing
Meanwhile, recent work has shown considerable improvements on many NLP tasks from model scaling.
Improving Compositional Generalization with Latent Structure and Data Augmentation
Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization.
Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks
Neural network models often generalize poorly to mismatched domains or distributions.
The Variability of Model Specification
It's regarded as an axiom that a good model is one that compromises between bias and variance.
Visually Grounded Concept Composition
We investigate ways to compose complex concepts in texts from primitive ones while grounding them in images.
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?
We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding.
Graph-Based Decoding for Task Oriented Semantic Parsing
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders.
Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations
Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but have been found to struggle at out-of-distribution compositional generalization.
Ranked #3 on
Semantic Parsing
on CFQ
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?
This has motivated new specialized architectures with stronger compositional biases, but most of these approaches have only been evaluated on synthetically-generated datasets, which are not representative of natural language variation.
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
We study the task of cross-database semantic parsing (XSP), where a system that maps natural language utterances to executable SQL queries is evaluated on databases unseen during training.
A Random Interaction Forest for Prioritizing Predictive Biomarkers
Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system.
Answering Conversational Questions on Structured Data without Logical Forms
We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation.
Generating Logical Forms from Graph Representations of Text and Entities
Structured information about entities is critical for many semantic parsing tasks.
Self-Attention with Relative Position Representations
On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1. 3 BLEU and 0. 3 BLEU over absolute position representations, respectively.
Ranked #22 on
Machine Translation
on WMT2014 English-French
