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Found 18 papers, 11 papers with code
MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models
Experiments on seven widely-used commonsense and math reasoning benchmarks show that MAGDi improves the reasoning capabilities of smaller models, outperforming several methods that distill from a single teacher and multiple teachers.
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
Large Language Models (LLMs) are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or taking into account multiple aspects and criteria.
ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
Large Language Models (LLMs) still struggle with natural language reasoning tasks.
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
We first show that teacher LLMs can indeed intervene on student reasoning to improve their performance.
ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness
Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear what constitutes a good reasoning chain and how to evaluate them.
MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation
We propose MURMUR, a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning.
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations
We observe that (1) GPT-3 explanations are as grammatical as human explanations regardless of the hardness of the test samples, (2) for easy examples, GPT-3 generates highly supportive explanations but human explanations are more generalizable, and (3) for hard examples, human explanations are significantly better than GPT-3 explanations both in terms of label-supportiveness and generalizability judgements.
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior.
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning
In this work, we study pre-trained language models that generate explanation graphs in an end-to-end manner and analyze their ability to learn the structural constraints and semantics of such graphs.
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning
In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph.
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning
Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context.
ConjNLI: Natural Language Inference Over Conjunctive Sentences
Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions in English and also how their usages and semantics differ from conjunctive and disjunctive boolean logic.
PRover: Proof Generation for Interpretable Reasoning over Rules
First, PROVER generates proofs with an accuracy of 87%, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6% improvement on zero-shot evaluation).
Pre-Training BERT on Domain Resources for Short Answer Grading
Pre-trained BERT contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data.
Learning Outcomes and Their Relatedness in a Medical Curriculum
A typical medical curriculum is organized in a hierarchy of instructional objectives called Learning Outcomes (LOs); a few thousand LOs span five years of study.
Joint Multi-Domain Learning for Automatic Short Answer Grading
On a large-scale industry dataset and a benchmarking dataset, we show that our model performs significantly better than existing techniques which either learn domain-specific models or adapt a generic similarity scoring model from a large corpus.
Open Information Extraction from Conjunctive Sentences
We demonstrate the value of our coordination analyzer in the end task of Open Information Extraction (Open IE).
Ranked #20 on
Open Information Extraction
on CaRB
Bootstrapping for Numerical Open IE
We design and release BONIE, the first open numerical relation extractor, for extracting Open IE tuples where one of the arguments is a number or a quantity-unit phrase.
