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Found 20 papers, 6 papers with code
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Contemporary language models enable new opportunities for structured reasoning with text, such as the construction and evaluation of intuitive, proof-like textual entailment trees without relying on brittle formal logic.
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
We evaluate our method in the classic videogame NetHack and the text environment ScienceWorld to demonstrate SSO's ability to optimize a set of skills and perform in-context policy improvement.
BaRDa: A Belief and Reasoning Dataset that Separates Factual Accuracy and Reasoning Ability
This shows the clear progression of models towards improved factual accuracy and entailment reasoning, and the dataset provides a new benchmark that more cleanly separates and quantifies these two notions.
CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
Language agents have shown some ability to interact with an external environment, e. g., a virtual world such as ScienceWorld, to perform complex tasks, e. g., growing a plant, without the startup costs of reinforcement learning.
Do language models have coherent mental models of everyday things?
Using these questions as probes, we observe that state-of-the-art pre-trained language models (LMs) like GPT-3 and Macaw have fragments of knowledge about these everyday things, but do not have fully coherent "parts mental models" (54-59% accurate, 19-43% conditional constraint violation).
Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE
We hypothesize that to perform this task well, the reader needs to mentally elaborate the scene being described to identify a sensible meaning of the language.
Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning
Our goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning.
Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement
Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time.
DREAM: Improving Situational QA by First Elaborating the Situation
To test this conjecture, we train a new model, DREAM, to answer questions that elaborate the scenes that situated questions are about, and then provide those elaborations as additional context to a question-answering (QA) model.
Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge
We present the ARC-DA dataset, a direct-answer ("open response", "freeform") version of the ARC (AI2 Reasoning Challenge) multiple-choice dataset.
ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language
In this work we show that a generative model, called ProofWriter, can reliably generate both implications of a theory and the natural language proof(s) that support them.
A Dataset for Tracking Entities in Open Domain Procedural Text
Our solution is a new task formulation where given just a procedural text as input, the task is to generate a set of state change tuples(entity, at-tribute, before-state, after-state)for each step, where the entity, attribute, and state values must be predicted from an open vocabulary.
Procedural Reading Comprehension with Attribute-Aware Context Flow
Procedural texts often describe processes (e. g., photosynthesis and cooking) that happen over entities (e. g., light, food).
Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text
Our goal is to better comprehend procedural text, e. g., a paragraph about photosynthesis, by not only predicting what happens, but why some actions need to happen before others.
WIQA: A dataset for "What if..." reasoning over procedural text
We introduce WIQA, the first large-scale dataset of "What if..." questions over procedural text.
From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project
This paper reports unprecedented success on the Grade 8 New York Regents Science Exam, where for the first time a system scores more than 90% on the exam's non-diagram, multiple choice (NDMC) questions.
Be Consistent! Improving Procedural Text Comprehension using Label Consistency
Our goal is procedural text comprehension, namely tracking how the properties of entities (e. g., their location) change with time given a procedural text (e. g., a paragraph about photosynthesis, a recipe).
Reasoning about Actions and State Changes by Injecting Commonsense Knowledge
Comprehending procedural text, e. g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at different timepoints can be answered.
Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension
The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full annotation of entity states (location and existence) during those changes (81k datapoints).
Ranked #4 on
Procedural Text Understanding
on ProPara
Domain-Targeted, High Precision Knowledge Extraction
Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject, predicate, object) statements about the world, in support of a downstream question-answering (QA) application.
