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Found 25 papers, 4 papers with code
Deeply Embedded Knowledge Representation & Reasoning For Natural Language Question Answering: A Practitioner’s Perspective
Successful application of Knowledge Representation and Reasoning (KR) in Natural Language Understanding (NLU) is largely limited by the availability of a robust and general purpose natural language parser.
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
In this paper, we introduce Direct Nash Optimization (DNO), a provable and scalable algorithm that marries the simplicity and stability of contrastive learning with theoretical generality from optimizing general preferences.
Orca-Math: Unlocking the potential of SLMs in Grade School Math
Ensembling provides a substantial boost in accuracy but at a significant cost increase with multiple calls to the model (e. g., Phi-GSM uses top-48 to boost the performance from 68. 2 to 81. 5).
Ranked #35 on
Arithmetic Reasoning
on GSM8K
Orca 2: Teaching Small Language Models How to Reason
Research on training small LMs has often relied on imitation learning to replicate the output of more capable models.
Ranked #1 on
Crass AI
on BIG-bench
Teaching Language Models to Hallucinate Less with Synthetic Tasks
We also find that optimizing the system message rather than the model weights can be critical; fine-tuning the entire model on the synthetic task can counterintuitively increase hallucination.
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
To address these challenges, we develop Orca (We are working with our legal team to publicly release a diff of the model weights in accordance with LLaMA's release policy to be published at https://aka. ms/orca-lm), a 13-billion parameter model that learns to imitate the reasoning process of LFMs.
Instruction Tuned Models are Quick Learners
In the MTL setting, an instruction tuned model trained on only 6% of downstream training data achieve SOTA, while using 100% of the training data results in a 3. 69% points improvement (ROUGE-L 74. 68) over the previous SOTA.
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks
Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems.
Improving Biomedical Information Retrieval with Neural Retrievers
We show that BM25 and our method can complement each other, and a simple hybrid model leads to further gains in the large corpus setting.
Commonsense Reasoning with Implicit Knowledge in Natural Language
Two common approaches to this are (i) Use of well-structured commonsense present in knowledge graphs, and (ii) Use of progressively larger transformer language models.
Information leak and incompatibility of physical context: A modified approach
The three primary limitations are that, (i) their approach is not positive operator-valued measurements and (ii), they restrict information-theoretic agents Alice, Eve and Bob to specific quantum operations and do not consider most general quantum operations i. e., quantum instruments and (iii), their measure of IPC can take negative values in specific cases in a more general scenario which implies the limitation of their information measure.
Quantum Physics Mathematical Physics Mathematical Physics
Layers of classicality in the compatibility of measurements
In particular we show that: (i) none of the layers of classicality respect transitivity property, (ii) the concept like degree of broadcasting similar to degree of compatibility does not exist, (iii) there exist informationally incomplete POVMs that are not individually broadcastable, (iv) a set of broadcasting channels can be obtained through concatenation of broadcasting and non-disturbing channels, (v) unlike compatibility, other layers of classicality are not convex, in general.
Quantum Physics Mathematical Physics Mathematical Physics
Can Transformers Reason About Effects of Actions?
A recent work has shown that transformers are able to "reason" with facts and rules in a limited setting where the rules are natural language expressions of conjunctions of conditions implying a conclusion.
Towards Question Format Independent Numerical Reasoning: A Set of Prerequisite Tasks
However, there exists a strong need for a benchmark which can evaluate the abilities of models, in performing question format independent numerical reasoning, as (i) the numerical reasoning capabilities we want to teach are not controlled by question formats, (ii) for numerical reasoning technology to have the best possible application, it must be able to process language and reason in a way that is not exclusive to a single format, task, dataset or domain.
Natural Language QA Approaches using Reasoning with External Knowledge
The challenges inspired by Winograd's councilmen example, and recent developments such as the Rebooting AI book, various NLQA datasets, research on knowledge acquisition in the NLQA context, and their use in various NLQA models have brought the issue of NLQA using ``reasoning'' with external knowledge to the forefront.
How Additional Knowledge can Improve Natural Language Commonsense Question Answering?
Recently several datasets have been proposed to encourage research in Question Answering domains where commonsense knowledge is expected to play an important role.
A Generate-Validate Approach to Answering Questions about Qualitative Relationships
Qualitative relationships describe how increasing or decreasing one property (e. g. altitude) affects another (e. g. temperature).
Careful Selection of Knowledge to solve Open Book Question Answering
Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic.
Ranked #23 on
Question Answering
on OpenBookQA
Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge
Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5. 53{\%} and 7. 7{\%} respectively.
Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming
While in recent years machine learning (ML) based approaches have been the popular approach in developing end-to-end question answering systems, such systems often struggle when additional knowledge is needed to correctly answer the questions.
Understanding Roles and Entities: Datasets and Models for Natural Language Inference
We present two new datasets and a novel attention mechanism for Natural Language Inference (NLI).
Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples
This paper is under consideration for acceptance in
