52 citations · 66 across the 15 of their papers we have counts for
12 papers · 1 filter
XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?
Akhila Yerukola, Jena D. Hwang, Mingqian Zheng +5
When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., "How do I parse XML with regex?"). In such cases, often referred to as the XY-prob…
Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication
Jocelyn Shen, Akhila Yerukola, Xuhui Zhou +3
Conversational breakdowns in close relationships are deeply shaped by personal histories and emotional context, yet most NLP research treats conflict detection as a general task, o…
Out of Style: RAG's Fragility to Linguistic Variation
Tianyu Cao, Neel Bhandari, Akhila Yerukola +2
Despite the impressive performance of Retrieval-augmented Generation (RAG) systems across various NLP benchmarks, their robustness in handling real-world user-LLM interaction queri…
PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages
Priyanshu Kumar, Devansh Jain, Akhila Yerukola +4
Truly multilingual safety moderation efforts for Large Language Models (LLMs) have been hindered by a narrow focus on a small set of languages (e.g., English, Chinese) as well as a…
Is the Pope Catholic? Yes, the Pope is Catholic. Generative Evaluation of Non-Literal Intent Resolution in LLMs
Akhila Yerukola, Saujas Vaduguru, Daniel Fried +1
Humans often express their communicative intents indirectly or non-literally, which requires their interlocutors -- human or AI -- to understand beyond the literal meaning of words…
NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models
Abhinav Rao, Akhila Yerukola, Vishwa Shah +2
To be effectively and safely deployed to global user populations, large language models (LLMs) may need to adapt outputs to user values and cultures, not just know about them. We i…
Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language
Jimin Mun, Emily Allaway, Akhila Yerukola +3
Counterspeech, i.e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship. Howeve…
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements
Xuhui Zhou, Hao Zhu, Akhila Yerukola +4
Warning: This paper contains content that may be offensive or upsetting. Understanding the harms and offensiveness of statements requires reasoning about the social and situational…
Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting
Akhila Yerukola, Xuhui Zhou, Elizabeth Clark +1
Most existing stylistic text rewriting methods and evaluation metrics operate on a sentence level, but ignoring the broader context of the text can lead to preferring generic, ambi…
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase
Akhila Yerukola, Mason Bretan, Hongxia Jin
We introduce a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. We compare…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…
Do Massively Pretrained Language Models Make Better Storytellers?
Abigail See, Aneesh Pappu, Rohun Saxena +2
Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks. However, the strength of these model…