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Found 8 papers, 5 papers with code
Making Retrieval-Augmented Language Models Robust to Irrelevant Context
An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant, and does not harm performance when it is not.
Ranked #3 on
Question Answering
on Bamboogle
Evaluating the Ripple Effects of Knowledge Editing in Language Models
This has led to the development of various editing methods that allow updating facts encoded by the model.
Answering Questions by Meta-Reasoning over Multiple Chains of Thought
Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer.
Ranked #2 on
Question Answering
on Bamboogle
QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs
Our results highlight the need for developing ODQA models that handle a broad range of question types, including single and multi-answer questions.
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification
Constructing benchmarks that test the abilities of modern natural language understanding models is difficult - pre-trained language models exploit artifacts in benchmarks to achieve human parity, but still fail on adversarial examples and make errors that demonstrate a lack of common sense.
SCROLLS: Standardized CompaRison Over Long Language Sequences
NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild.
Ranked #8 on
Long-range modeling
on SCROLLS
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills
Models pre-trained with a language modeling objective possess ample world knowledge and language skills, but are known to struggle in tasks that require reasoning.
MultiModalQA: Complex Question Answering over Text, Tables and Images
When answering complex questions, people can seamlessly combine information from visual, textual and tabular sources.
