Papers

Learn more about AI2's Lasting Impact Award
Viewing 1-10 of 106 papers
  • Making Retrieval-Augmented Language Models Robust to Irrelevant Context

    Ori Yoran, Tomer Wolfson, Ori Ram, Jonathan BerantarXiv.org2023 Retrieval-augmented language models (RALMs) hold promise to produce language understanding systems that are are factual, efficient, and up-to-date. An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant…
  • Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents

    Catherine Chen, Zejiang Shen, Dan Klein, Gabi Stanovsky, Doug Downey, Kyle LoFindings of ACL2023 Recent work has shown that infusing layout features into language models (LMs) improves processing of visually-rich documents such as scientific papers. Layout-infused LMs are often evaluated on documents with familiar layout features (e.g., papers from the…
  • From Centralized to Ad-Hoc Knowledge Base Construction for Hypotheses Generation.

    Shaked Launer-Wachs, Hillel Taub-Tabib, Jennie Tokarev Madem, Orr Bar-Natan, Yoav Goldberg, Y. ShamayJournal of Biomedical Informatics2023 Objective To demonstrate and develop an approach enabling individual researchers or small teams to create their own ad-hoc, lightweight knowledge bases tailored for specialized scientific interests, using text-mining over scientific literature, and…
  • Answering Questions by Meta-Reasoning over Multiple Chains of Thought

    Ori Yoran, Tomer Wolfson, Ben Bogin, Uri Katz, Daniel Deutch, Jonathan BerantEMNLP2023 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. Often, multiple chains are sampled and aggregated through a voting mechanism…
  • Lexical Generalization Improves with Larger Models and Longer Training

    Elron Bandel, Yoav Goldberg, Yanai ElazarFinding of EMNLP2022 While fine-tuned language models perform well on many tasks, they were also shown to rely on superficial surface features such as lexical overlap. Excessive utilization of such heuristics can lead to failure on challenging inputs. We analyze the use of lexical…
  • Linear Adversarial Concept Erasure

    Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan CotterellICML2022 We formulate the problem of identifying and erasing a linear subspace that corresponds to a given concept, in order to prevent linear predictors from recovering the concept. We model this problem as a constrained, linear minimax game, and show that existing…
  • A Dataset for N-ary Relation Extraction of Drug Combinations

    Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni, Dana Azagury, Yosi Shamay, Hillel Taub-Tabib, Tom Hope, Yoav GoldbergNAACL2022 Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available…
  • Weakly Supervised Text-to-SQL Parsing through Question Decomposition

    Tomer Wolfson, Daniel Deutch, Jonathan BerantFindings of NAACL2022 Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries. In this work, we…
  • Draw Me a Flower: Grounding Formal Abstract Structures Stated in Informal Natural Language

    Royi Lachmy, Valentina Pyatkin, Reut TsarfatyACL2022 Forming and interpreting abstraction is a core process in human communication. In particular, when giving and performing complex instructions stated in natural language (NL), people may naturally evoke abstract constructs such as objects, loops, conditions…
  • Large Scale Substitution-based Word Sense Induction

    Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, Yoav GoldbergACL2022 We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where…