Papers by Akhila Yerukola
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements (2023.findings-acl)
Copied to clipboard
Xuhui Zhou, Hao Zhu, Akhila Yerukola, Thomas Davidson, Jena D. Hwang, Swabha Swayamdipta, Maarten Sap
| Challenge: | Understanding the harms and offensiveness of statements requires reasoning about the social and situational context in which they are made. |
| Approach: | They propose a context-aware formalism for explaining the intents, reactions, and harms of offensive statements grounded in their social and situational contexts. |
| Outcome: | The proposed framework is the first context-aware formalism for explaining the intents, reactions, and harms of offensive statements grounded in their social and situational context. |
Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Counterspeech, i.e. responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship risks of deletion-based content moderation. |
| Approach: | They draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. |
| Outcome: | The strategies used in human- and machine-generated counterspeech datasets are convincing, whereas human-written counterspech uses less specific strategies compared to machine-produced counters. |
Is the Pope Catholic? Yes, the Pope is Catholic. Generative Evaluation of Non-Literal Intent Resolution in LLMs (2024.acl-short)
Copied to clipboard
| Challenge: | Existing work on discriminative evaluations of large language models has focused on discrimination, but this paper examines their intention understanding by examining their responses to non-literal utterances. |
| Approach: | They propose a framework to evaluate large language models’ intention understanding by examining their responses to non-literal utterances. |
| Outcome: | The proposed framework compares large language models' responses to human-like expectations and provides nuanced evaluations of their intention understanding. |
Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication (2025.emnlp-main)
Copied to clipboard
| Challenge: | a recent study examines the role of personalization in enabling LLMs to serve as effective mediators in human communication for authentic connection. |
| Approach: | They leverage nonviolent communication theory to evaluate LLMs in detecting conversational breakdowns . they annotate a subset of dialogues and obtain fine-grained labels of communication breakdown types . |
| Outcome: | The proposed dataset analyzes human interactions and relationships in a human context. |
Mind the Gesture: Evaluating AI Sensitivity to Culturally Offensive Non-Verbal Gestures (2025.acl-long)
Copied to clipboard
| Challenge: | a dataset of 288 gesture-country pairs is used to evaluate AI systems' cultural awareness of offensive gestures and nonverbal signs. |
| Approach: | They use a dataset of 288 gesture-country pairs annotated for offensiveness, cultural significance, and contextual factors across 25 gestures and 85 countries. |
| Outcome: | The proposed dataset analyzes 288 gesture-country pairs across 25 gestures and 85 countries. |
NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are widely used and engage millions of users from diverse contexts and cultures. |
| Approach: | They propose an evaluation framework to assess LLMs’ cultural adaptability by measuring their ability to judge social acceptability across varying levels of cultural norm specificity. |
| Outcome: | The proposed model shows stronger adaptability to English-centric cultures over those from the Global South. |
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase (2021.eacl-main)
Copied to clipboard
| Challenge: | a data augmentation technique is used to boost performance on spoken language understanding tasks. |
| Approach: | They propose a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. |
| Outcome: | The proposed method performs well on domain and intent classification tasks for a voice assistant and in a user-study focused on utterance naturalness and semantic similarity. |
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods analyze and compute features collectively for all slot types, and have no way to explain slot filling model decisions. |
| Approach: | They propose a method that learns to generate additional slot type specific features to improve accuracy and provides explanations for slot filling decisions for the first time in a joint NLU model. |
| Outcome: | The proposed model improves on two widely used datasets and provides an explanation for slot filling decisions for the first time. |
Don’t Take This Out of Context!: On the Need for Contextual Models and Evaluations for Stylistic Rewriting (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing stylistic text rewriting methods ignore the context of the text, causing generic, incoherent, and generic outputs. |
| Approach: | They propose a contextual evaluation metric that integrates preceding context into stylistic text rewriting. |
| Outcome: | The proposed metric integrates the preceding textual context into rewriting and evaluation stages . human preferences are better reflected by the proposed criterio and other metrics . |
Out of Style: RAG’s Fragility to Linguistic Variation (2026.eacl-long)
Copied to clipboard
| Challenge: | linguistic reformulations impact both retrieval and generation stages, leading to a relative performance drop of up to 40.41% for less formal queries and 38.86% for queries containing grammatical errors. |
| Approach: | They evaluate two retrieval models and nine LLMs across four QA datasets and examine how linguistic reformulations impact RAG performance. |
| Outcome: | The proposed models show that linguistic reformulations significantly impact both retrieval and generation stages, leading to a performance drop of up to 40.41% for less formal queries and 38.86% for queries containing grammatical errors. |