Papers by Afra Feyza Akyürek

5 papers
On Measuring Social Biases in Prompt-Based Multi-Task Learning (2022.findings-naacl)

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Challenge: a large body of work within prompt engineering attempts to understand the effects of input forms and prompts in achieving superior performance.
Approach: They propose a large-scale text-to-text language model trained using prompts . they consider two different forms of semantically equivalent inputs - question-answer format and premise-hypothesis format .
Outcome: The proposed model can generalize into novel forms of language and handle novel tasks.
PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning (2026.acl-long)

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Challenge: Frontier models often lack a view of performance on open-ended, economically consequential tasks in high-stakes professional domains where practical returns matter most.
Approach: They introduce a professional reasoning benchmark that recruits 182 qualified professionals to contribute questions inspired by their workflows.
Outcome: The proposed model outperforms other models in 114 countries and 47 US jurisdictions on hard subsets.
IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words (2021.findings-acl)

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Challenge: Existing research on word normalization in Indonesian language relies on static dictionaries and machine translation.
Approach: They propose to use Twitter to annotate Indonesian colloquial words with their standard forms and their word formation types/tags to perform morphological word normalization.
Outcome: The proposed dataset analyzes morphological word normalization on Indonesian colloquial Lexicons and provides a baseline for future work.
Multi-Label and Multilingual News Framing Analysis (2020.acl-main)

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Challenge: Recent studies have focused on news framing in English, but few studies have explored how it can be extended to other languages and in multi-label settings.
Approach: They propose a method that leverages dictionary and few annotations to detect frames from just the headline in a low-resource context.
Outcome: The proposed method performs better than translating the entire headline to the source language . it can be scaled up to many languages, even those without existing translation technologies .
Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability (2024.findings-acl)

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Challenge: Existing language models (LMs) generate factually correct text and estimate truth values of individual claims, but they do not reflect a coherent, manipulable model of the world.
Approach: They propose a method that uses language models to identify implications of (and contradictions within) the text they generate.
Outcome: The proposed method improves LM factuality by 3-26% across the CREAK, MQuAKE, and Reversal Curse datasets.

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