Search Results for author:
Found 27 papers, 21 papers with code
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity.
Policy Improvement using Language Feedback Models
We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instruction following.
Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning
Unlike inverse RL and recent work that uses LLMs to write sparse reward codes, Text2Reward produces interpretable, free-form dense reward codes that cover a wide range of tasks, utilize existing packages, and allow iterative refinement with human feedback.
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge.
RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering
Moreover, existing models are not robust to variations in question constraints, but can be made more robust by tuning on clusters of related questions.
M2D2: A Massively Multi-domain Language Modeling Dataset
We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs).
Improving Policy Learning via Language Dynamics Distillation
Recent work has shown that augmenting environments with language descriptions improves policy learning.
Improving Intrinsic Exploration with Language Abstractions
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark
We hope SILG enables the community to quickly identify new methodolo- gies for language grounding that generalize to a diverse set of environments and their associated challenges.
SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark
We hope SILG enables the community to quickly identify new methodologies for language grounding that generalize to a diverse set of environments and their associated challenges.
LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer
Moreover, compared to previous methods on unsupervised data synthesis, our method results in higher quality parallel style pairs and improves model performance.
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
We investigate the use of natural language to drive the generalization of control policies and introduce the new multi-task environment Messenger with free-form text manuals describing the environment dynamics.
Grounded Adaptation for Zero-shot Executable Semantic Parsing
We propose Grounded Adaptation for Zero-shot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e. g. new database schemas).
Ranked #6 on
Text-To-SQL
on SParC
RTFM: Generalising to New Environment Dynamics via Reading
In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.
RTFM: Generalising to Novel Environment Dynamics via Reading
In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.
E3: Entailment-driven Extracting and Editing for Conversational Machine Reading
Conversational machine reading systems help users answer high-level questions (e. g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made(e. g. whether they need certain income levels or veteran status).
Multi-hop Reading Comprehension through Question Decomposition and Rescoring
Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs.
Ranked #65 on
Question Answering
on HotpotQA
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document.
Ranked #5 on
Question Answering
on WikiHop
Global-Locally Self-Attentive Encoder for Dialogue State Tracking
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
Automatic Speech Recognition (ASR)
Dialogue State Tracking
+3
Efficient and Robust Question Answering from Minimal Context over Documents
Neural models for question answering (QA) over documents have achieved significant performance improvements.
Ranked #3 on
Question Answering
on NewsQA
Global-Locally Self-Attentive Dialogue State Tracker
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate.
Ranked #28 on
Question Answering
on SQuAD1.1 dev
Position-aware Attention and Supervised Data Improve Slot Filling
The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.
Ranked #7 on
Relation Extraction
on Re-TACRED
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
A significant amount of the world's knowledge is stored in relational databases.
Ranked #9 on
Code Generation
on WikiSQL
Dynamic Coattention Networks For Question Answering
Several deep learning models have been proposed for question answering.
Ranked #2 on
Open-Domain Question Answering
on SQuAD1.1
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
Ranked #66 on
Sentiment Analysis
on SST-2 Binary classification
