Search Results for author: Tom Mitchell

Found 41 papers, 12 papers with code

AgentKit: Flow Engineering with Graphs, not Coding

1 code implementation17 Apr 2024 Yue Wu, Yewen Fan, So Yeon Min, Shrimai Prabhumoye, Stephen Mcaleer, Yonatan Bisk, Ruslan Salakhutdinov, Yuanzhi Li, Tom Mitchell

The chains of nodes can be designed to explicitly enforce a naturally structured "thought process".

Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions

no code implementations2 Feb 2024 Pouya Pezeshkpour, Eser Kandogan, Nikita Bhutani, Sajjadur Rahman, Tom Mitchell, Estevam Hruschka

We present a formal definition of reasoning capacity and illustrate its utility in identifying limitations within each component of the system.

Ruffle&Riley: Towards the Automated Induction of Conversational Tutoring Systems

no code implementations26 Sep 2023 Robin Schmucker, Meng Xia, Amos Azaria, Tom Mitchell

Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interaction.

The Internal State of an LLM Knows When It's Lying

1 code implementation26 Apr 2023 Amos Azaria, Tom Mitchell

While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone.


Zero-shot Triplet Extraction by Template Infilling

1 code implementation21 Dec 2022 Bosung Kim, Hayate Iso, Nikita Bhutani, Estevam Hruschka, Ndapa Nakashole, Tom Mitchell

We propose a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that aligns the task objective to the pre-training objective of generative transformers to generalize to unseen relations.

Data Augmentation Language Modelling +2

Interactive Task Learning from GUI-Grounded Natural Language Instructions and Demonstrations

1 code implementation ACL 2020 Toby Jia-Jun Li, Tom Mitchell, Brad Myers

We show SUGILITE, an intelligent task automation agent that can learn new tasks and relevant associated concepts interactively from the user{'}s natural language instructions and demonstrations, using the graphical user interfaces (GUIs) of third-party mobile apps.

Conversational Neuro-Symbolic Commonsense Reasoning

1 code implementation17 Jun 2020 Forough Arabshahi, Jennifer Lee, Mikayla Gawarecki, Kathryn Mazaitis, Amos Azaria, Tom Mitchell

More precisely, we consider the problem of identifying the unstated presumptions of the speaker that allow the requested action to achieve the desired goal from the given state (perhaps elaborated by making the implicit presumptions explicit).

Jelly Bean World: A Testbed for Never-Ending Learning

3 code implementations ICLR 2020 Emmanouil Antonios Platanios, Abulhair Saparov, Tom Mitchell

Never-ending learning is a machine learning paradigm that aims to bridge this gap, with the goal of encouraging researchers to design machine learning systems that can learn to perform a wider variety of inter-related tasks in more complex environments.

BIG-bench Machine Learning Navigate

Game Design for Eliciting Distinguishable Behavior

no code implementations NeurIPS 2019 Fan Yang, Liu Leqi, Yifan Wu, Zachary C. Lipton, Pradeep Ravikumar, William W. Cohen, Tom Mitchell

The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems.

Learning to Ask for Conversational Machine Learning

no code implementations IJCNLP 2019 Shashank Srivastava, Igor Labutov, Tom Mitchell

Natural language has recently been explored as a new medium of supervision for training machine learning models.

BIG-bench Machine Learning

Learning Data Manipulation for Augmentation and Weighting

2 code implementations NeurIPS 2019 Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom Mitchell, Eric P. Xing

In this work, we propose a new method that supports learning different manipulation schemes with the same gradient-based algorithm.

Data Augmentation Reinforcement Learning (RL) +2

Look-up and Adapt: A One-shot Semantic Parser

1 code implementation IJCNLP 2019 Zhichu Lu, Forough Arabshahi, Igor Labutov, Tom Mitchell

In this paper, we propose a semantic parser that generalizes to out-of-domain examples by learning a general strategy for parsing an unseen utterance through adapting the logical forms of seen utterances, instead of learning to generate a logical form from scratch.

Relating Simple Sentence Representations in Deep Neural Networks and the Brain

1 code implementation ACL 2019 Sharmistha Jat, Hao Tang, Partha Talukdar, Tom Mitchell

To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence.


Understanding language-elicited EEG data by predicting it from a fine-tuned language model

no code implementations NAACL 2019 Dan Schwartz, Tom Mitchell

This new approach to analysis shows for the first time that all of the ERPs are predictable from embeddings of a stream of language.


Leveraging Knowledge Bases in LSTMs for Improving Machine Reading

no code implementations ACL 2017 Bishan Yang, Tom Mitchell

This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading.

Entity Extraction using GAN Event Extraction +2

Contextual Parameter Generation for Universal Neural Machine Translation

1 code implementation EMNLP 2018 Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig, Tom Mitchell

We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation.

Domain Adaptation Machine Translation +2

Joint Extraction of Events and Entities within a Document Context

1 code implementation NAACL 2016 Bishan Yang, Tom Mitchell

Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon.

Entity Extraction using GAN Event Extraction +1

Inferring Interpersonal Relations in Narrative Summaries

no code implementations1 Dec 2015 Shashank Srivastava, Snigdha Chaturvedi, Tom Mitchell

In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries.

Clustering Structured Prediction

Sense Discovery via Co-Clustering on Images and Text

no code implementations CVPR 2015 Xinlei Chen, Alan Ritter, Abhinav Gupta, Tom Mitchell

We present a co-clustering framework that can be used to discover multiple semantic and visual senses of a given Noun Phrase (NP).


Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic

no code implementations12 Apr 2014 William Yang Wang, Kathryn Mazaitis, Ni Lao, Tom Mitchell, William W. Cohen

We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank (PPR) on a linearized version of the proof space, and using on this connection, we develop a proveably-correct approximate grounding scheme, based on the PageRank-Nibble algorithm.

Relational Reasoning

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