111 citations · 303 across the 16 of their papers we have counts for
18 papers
Recursive self-improvement of AI research agents
Dhruv Srikanth, Bingchen Zhao, Dixing Xu +2
AI agents are beginning to automate research and development across the AI stack, from improving training efficiency to optimizing inference. A natural next step is to improve the…
AutoData: Agentic Search for Pre-training Data Selection
Yan Meng, Dhruv Srikanth, Bingchen Zhao +2
LLM agents have recently shown promise in automating machine learning engineering by editing model and training code under execution feedback. Data, however, remains largely outsid…
SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents
Bingchen Zhao, Dhruv Srikanth, Yuxiang Wu +1
As long-horizon coding agents produce more code than any developer can review, oversight collapses onto a single surface: the automated test suite. Reward hacking naturally arises…
AIDE: AI-Driven Exploration in the Space of Code
Zhengyao Jiang, Dominik Schmidt, Dhruv Srikanth +4
Machine learning, the foundation of modern artificial intelligence, has driven innovations that have fundamentally transformed the world. Yet, behind advancements lies a complex an…
Analysing The Impact of Sequence Composition on Language Model Pre-Training
Yu Zhao, Yuanbin Qu, Konrad Staniszewski +5
Most language model pre-training frameworks concatenate multiple documents into fixed-length sequences and use causal masking to compute the likelihood of each token given its cont…
Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration
Zhenran Xu, Senbao Shi, Baotian Hu +4
Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have ex…
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
Yuxiang Wu, Guanting Dong, Weiran Xu
Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time-consuming and costly. However, DST extends be…
Using Natural Language Explanations to Improve Robustness of In-context Learning
Xuanli He, Yuxiang Wu, Oana-Maria Camburu +2
Recent studies demonstrated that large language models (LLMs) can excel in many tasks via in-context learning (ICL). However, recent works show that ICL-prompted models tend to pro…
Towards Fine-grained Causal Reasoning and QA
Linyi Yang, Zhen Wang, Yuxiang Wu +2
Understanding causality is key to the success of NLP applications, especially in high-stakes domains. Causality comes in various perspectives such as enable and prevent that, despi…
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
Yuxiang Wu, Matt Gardner, Pontus Stenetorp +1
Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they a…
Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints
Yuxiang Wu, Pasquale Minervini, Pontus Stenetorp +1
Adaptive Computation (AC) has been shown to be effective in improving the efficiency of Open-Domain Question Answering (ODQA) systems. However, current AC approaches require tuning…
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Patrick Lewis, Yuxiang Wu, Linqing Liu +5
Open-domain Question Answering models which directly leverage question-answer (QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in terms of spee…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
Don't Read Too Much into It: Adaptive Computation for Open-Domain Question Answering
Yuxiang Wu, Sebastian Riedel, Pasquale Minervini +1
Most approaches to Open-Domain Question Answering consist of a light-weight retriever that selects a set of candidate passages, and a computationally expensive reader that examines…
How Context Affects Language Models' Factual Predictions
Fabio Petroni, Patrick Lewis, Aleksandra Piktus +4
When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for…
Language Models as Knowledge Bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis +4
Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models…
Learning to Extract Coherent Summary via Deep Reinforcement Learning
Yuxiang Wu, Baotian Hu
Coherence plays a critical role in producing a high-quality summary from a document. In recent years, neural extractive summarization is becoming increasingly attractive. However,…
Integrating User and Agent Models: A Deep Task-Oriented Dialogue System
Weiyan Wang, Yuxiang WU, Yu Zhang +3
Task-oriented dialogue systems can efficiently serve a large number of customers and relieve people from tedious works. However, existing task-oriented dialogue systems depend on h…