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Found 13 papers, 11 papers with code
IrEne-viz: Visualizing Energy Consumption of Transformer Models
IrEne is an energy prediction system that accurately predicts the interpretable inference energy consumption of a wide range of Transformer-based NLP models.
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
While using the question to retrieve relevant text from an external knowledge source helps LLMs, we observe that this one-step retrieve-and-read approach is insufficient for multi-step QA.
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions
These findings suggest that, in the case of answering reading comprehension questions, debate is not a helpful format.
Decomposed Prompting: A Modular Approach for Solving Complex Tasks
On symbolic reasoning tasks, we can further decompose sub-tasks that are hard for LLMs into even simpler solvable sub-tasks.
Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts
We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion.
Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions
We use long contexts -- humans familiar with the context write convincing explanations for pre-selected correct and incorrect answers, and we test if those explanations allow humans who have not read the full context to more accurately determine the correct answer.
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension
Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system.
MuSiQue: Multihop Questions via Single-hop Question Composition
Multihop reasoning remains an elusive goal as existing multihop benchmarks are known to be largely solvable via shortcuts.
IrEne: Interpretable Energy Prediction for Transformers
We present IrEne, an interpretable and extensible energy prediction system that accurately predicts the inference energy consumption of a wide range of Transformer-based NLP models.
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?
However, we find that training crowdworkers, and then using an iterative process of collecting data, sending feedback, and qualifying workers based on expert judgments is an effective means of collecting challenging data.
Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning
For a recent large-scale model (XLNet), we show that only 18 points out of its answer F1 score of 72 on HotpotQA are obtained through multifact reasoning, roughly the same as that of a simpler RNN baseline.
DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering
It turns out that we can get by without input-wide self-attention at all layers, especially in the lower layers.
Repurposing Entailment for Multi-Hop Question Answering Tasks
We introduce Multee, a general architecture that can effectively use entailment models for multi-hop QA tasks.
