Search Results for author: Emmy Liu

Found 14 papers, 8 papers with code

An Incomplete Loop: Deductive, Inductive, and Abductive Learning in Large Language Models

no code implementations3 Apr 2024 Emmy Liu, Graham Neubig, Jacob Andreas

Modern language models (LMs) can learn to perform new tasks in different ways: in instruction following, the target task is described explicitly in natural language; in few-shot prompting, the task is specified implicitly with a small number of examples; in instruction inference, LMs are presented with in-context examples and are then prompted to generate a natural language task description before making predictions.

Instruction Following Machine Translation

Program-Aided Reasoners (better) Know What They Know

1 code implementation16 Nov 2023 Anubha Kabra, Sanketh Rangreji, Yash Mathur, Aman Madaan, Emmy Liu, Graham Neubig

Our analysis uncovers that prompting styles that produce lesser diversity in generations also have more calibrated results, and thus we also experiment with inducing lower generation diversity using temperature scaling and find that for certain temperatures, PAL is not only more accurate but is also more calibrated than COT.

Divergences between Language Models and Human Brains

1 code implementation15 Nov 2023 Yuchen Zhou, Emmy Liu, Graham Neubig, Michael J. Tarr, Leila Wehbe

In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories.

Emotional Intelligence

Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting

1 code implementation10 Oct 2023 Emmy Liu, Aditi Chaudhary, Graham Neubig

Idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts.

4k Machine Translation +2

Syntax and Semantics Meet in the "Middle": Probing the Syntax-Semantics Interface of LMs Through Agentivity

1 code implementation29 May 2023 Lindia Tjuatja, Emmy Liu, Lori Levin, Graham Neubig

Recent advances in large language models have prompted researchers to examine their abilities across a variety of linguistic tasks, but little has been done to investigate how models handle the interactions in meaning across words and larger syntactic forms -- i. e. phenomena at the intersection of syntax and semantics.

Computational Language Acquisition with Theory of Mind

1 code implementation2 Mar 2023 Andy Liu, Hao Zhu, Emmy Liu, Yonatan Bisk, Graham Neubig

We also find some evidence that increasing task difficulty in the training process results in more fluent and precise utterances in evaluation.

Language Acquisition

Are Representations Built from the Ground Up? An Empirical Examination of Local Composition in Language Models

1 code implementation7 Oct 2022 Emmy Liu, Graham Neubig

We find that the representation of a parent phrase can be predicted with some accuracy given an affine transformation of its children.

Open-Ended Question Answering

Assessing Group-level Gender Bias in Professional Evaluations: The Case of Medical Student End-of-Shift Feedback

no code implementations NAACL (GeBNLP) 2022 Emmy Liu, Michael Henry Tessler, Nicole Dubosh, Katherine Mosher Hiller, Roger Levy

Although approximately 50% of medical school graduates today are women, female physicians tend to be underrepresented in senior positions, make less money than their male counterparts and receive fewer promotions.

Topic Models

Testing the Ability of Language Models to Interpret Figurative Language

2 code implementations NAACL 2022 Emmy Liu, Chen Cui, Kenneth Zheng, Graham Neubig

Figurative and metaphorical language are commonplace in discourse, and figurative expressions play an important role in communication and cognition.

Open-Ended Question Answering

Statistical Consequences of Dueling Bandits

no code implementations16 Oct 2021 Nayan Saxena, Pan Chen, Emmy Liu

Multi-Armed-Bandit frameworks have often been used by researchers to assess educational interventions, however, recent work has shown that it is more beneficial for a student to provide qualitative feedback through preference elicitation between different alternatives, making a dueling bandits framework more appropriate.

When Does Translation Require Context? A Data-driven, Multilingual Exploration

no code implementations15 Sep 2021 Patrick Fernandes, Kayo Yin, Emmy Liu, André F. T. Martins, Graham Neubig

Although proper handling of discourse significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation quality metrics.

Machine Translation Translation

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