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Found 24 papers, 20 papers with code
WebArena: A Realistic Web Environment for Building Autonomous Agents
Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions.
Hierarchical Prompting Assists Large Language Model on Web Navigation
Large language models (LLMs) struggle on processing complicated observations in interactive decision making tasks.
Active Retrieval Augmented Generation
In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation.
Why do Nearest Neighbor Language Models Work?
Language models (LMs) compute the probability of a text by sequentially computing a representation of an already-seen context and using this representation to predict the next word.
DocPrompting: Generating Code by Retrieving the Docs
Publicly available source-code libraries are continuously growing and changing.
MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages
While there has been a recent burgeoning of applications at the intersection of natural and programming languages, such as code generation and code summarization, these applications are usually English-centric.
A Systematic Evaluation of Large Language Models of Code
We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various programming languages.
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time.
Capturing Structural Locality in Non-parametric Language Models
Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies.
Learning Structural Edits via Incremental Tree Transformations
To show the unique benefits of modeling tree edits directly, we further propose a novel edit encoder for learning to represent edits, as well as an imitation learning method that allows the editor to be more robust.
In-IDE Code Generation from Natural Language: Promise and Challenges
A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs.
Code Generation
Data Visualization
Software Engineering
A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos
Watching instructional videos are often used to learn about procedures.
Minimally Supervised Categorization of Text with Metadata
Then, based on the same generative process, we synthesize training samples to address the bottleneck of label scarcity.
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents.
Ranked #3 on
Code Generation
on CoNaLa-Ext
How Can We Know What Language Models Know?
Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profession".
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
With the massive number of repositories available, there is a pressing need for topic-based search.
StateLens: A Reverse Engineering Solution for Making Existing Dynamic Touchscreens Accessible
Second, using the state diagrams, StateLens automatically generates conversational agents to guide blind users through specifying the tasks that the interface can perform, allowing the StateLens iOS application to provide interactive guidance and feedback so that blind users can access the interface.
AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging
We introduce an open-source web-based data annotation framework (AlpacaTag) for sequence tagging tasks such as named-entity recognition (NER).
Data-to-Text Generation with Style Imitation
That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.
ExtRA: Extracting Prominent Review Aspects from Customer Feedback
Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects.
Mining Cross-Cultural Differences and Similarities in Social Media
Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media.
Automatic Extraction of Commonsense LocatedNear Knowledge
LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life.
Indirect Supervision for Relation Extraction using Question-Answer Pairs
However, due to the incompleteness of knowledge bases and the context-agnostic labeling, the training data collected via distant supervision (DS) can be very noisy.
Empower Sequence Labeling with Task-Aware Neural Language Model
In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task.
Ranked #13 on
Part-Of-Speech Tagging
on Penn Treebank
