19 citations · 19 across the 11 of their papers we have counts for
12 papers
Beyond Better Ideas: How Human-AI Collaboration Shapes Scientific Ideas and Human Perceptions
Qian Chen, Saatvik Pradhan, Janice Ahn +7
As AI increasingly becomes a collaborator in scientific research, its impact may extend beyond improving ideas to reshaping how they are evaluated, attributed, and perceived by hum…
Model Unlearning Objectives Vary for Distinct Language Functions
Berk Atil, Vipul Gupta, Rebecca J. Passonneau
Large language models (LLMs) learn undesirable properties during pretraining, including dangerous knowledge and toxic text generation. Just as post-training uses different objectiv…
Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning
Jihyun Janice Ahn, Ryo Kamoi, Berk Atil +34
LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify…
Robust Persona-Aware Toxicity Detection with Prompt Optimization and Learned Ensembling
Berk Atil, Rebecca J. Passonneau, Ninareh Mehrabi
Toxicity detection is inherently subjective, shaped by the diverse perspectives and social priors of different demographic groups. While ``pluralistic'' modeling as used in economi…
Self-Explaining Hate Speech Detection with Moral Rationales
Francielle Vargas, Jackson Trager, Diego Alves +6
Existing hate speech detection models are often opaque and rely on surface-level lexical cues, which makes them vulnerable to spurious correlations and limits robustness, interpret…
Do Methods to Jailbreak and Defend LLMs Generalize Across Languages?
Berk Atil, Rebecca J. Passonneau, Fred Morstatter
Large language models (LLMs) undergo safety alignment after training and tuning, yet recent work shows that safety can be bypassed through jailbreak attacks. While many jailbreaks…
Something Just Like TRuST : Toxicity Recognition of Span and Target
Berk Atil, Namrata Sureddy, Rebecca J. Passonneau
Toxic language includes content that is offensive, abusive, or that promotes harm. Progress in preventing toxic output from large language models (LLMs) is hampered by inconsistent…
Can LLMs Rank the Harmfulness of Smaller LLMs? We are Not There Yet
Berk Atil, Vipul Gupta, Sarkar Snigdha Sarathi Das +1
Large language models (LLMs) have become ubiquitous, thus it is important to understand their risks and limitations. Smaller LLMs can be deployed where compute resources are constr…
Non-Determinism of "Deterministic" LLM Settings
Berk Atil, Sarp Aykent, Alexa Chittams +10
LLM (large language model) practitioners commonly notice that outputs can vary for the same inputs under settings expected to be deterministic. Yet the questions of how pervasive t…
VerAs: Verify then Assess STEM Lab Reports
Berk Atil, Mahsa Sheikhi Karizaki, Rebecca J. Passonneau
With an increasing focus in STEM education on critical thinking skills, science writing plays an ever more important role in curricula that stress inquiry skills. A recently publis…
Look back, look around: a systematic analysis of effective predictors for new outlinks in focused Web crawling
Thi Kim Nhung Dang, Doina Bucur, Berk Atil +4
Small and medium enterprises rely on detailed Web analytics to be informed about their market and competition. Focused crawlers meet this demand by crawling and indexing specific p…
DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models
Rıza Özçelik, Alperen Bağ, Berk Atıl +3
Computational models that accurately predict the binding affinity of an input protein-chemical pair can accelerate drug discovery studies. These models are trained on available pro…