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Found 7 papers, 5 papers with code
LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
(2) We develop LLM Reasoners, a library for standardized modular implementation of existing and new reasoning algorithms, under a unified formulation of the search, reward, and world model components.
Extracting Mathematical Concepts with Large Language Models
Where our study diverges from previous work is in (1) providing a more thorough analysis of what makes mathematical term extraction a difficult problem to begin with; (2) paying close attention to inter-annotator disagreements; (3) providing a set of guidelines which both human and machine annotators could use to standardize the extraction process; (4) introducing a new annotation tool to help humans with ATE, applicable to any mathematical field and even beyond mathematics; (5) using prompts to ChatGPT as part of the extraction process, and proposing best practices for such prompts; and (6) raising the question of whether ChatGPT could be used as an annotator on the same level as human experts.
DISCO: Distilling Counterfactuals with Large Language Models
However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale.
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding
In the age of large transformer language models, linguistic evaluation play an important role in diagnosing models' abilities and limitations on natural language understanding.
Probing Linguistic Information For Logical Inference In Pre-trained Language Models
We propose a methodology for probing linguistic information for logical inference in pre-trained language model representations.
NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning
Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference (NLI).
Ranked #1 on
Natural Language Inference
on MED
Monotonicity Marking from Universal Dependency Trees
Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics.
