230 citations · 311 across the 33 of their papers we have counts for
5 papers · 1 filter
Guilt by Association: Semantic Leakage Drives Associative Gender Bias
Hazel Chen, Peter West, Hila Gonen
Semantic leakage occurs when a concept in the prompt steers model generation about an entity, even though the concept is irrelevant to it (e.g., a person who likes yellow is said t…
Multi-LLM Collaborative Alignment via Stackelberg Games
Christina Hahn, Shangbin Feng, Dean Light +3
A pool of language models can collaborate and improve collectively by learning from one another's responses. These interactions depend on the instructions used during training. Exi…
Don't `Well, Actually' Me Unless You Know What You're Talking About: Weak Presupposition Verification Degrades General QA Performance
Shenran Wang, Vered Shwartz, Hila Gonen
False-presupposition QA (FPQA) tests LLMs on their ability to identify false presuppositions in questions and abstain or correct them rather than reinforcing false assumptions. The…
PhantomBench: Benchmarking the Non-existential Threat of Language Models
Haeji Jung, Hila Gonen
Hallucinations, where language models (LMs) generate factually ungrounded responses, pose serious risks, as users tend to blindly rely on them. This is particularly concerning in h…
Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmark
Terra Blevins, Stephen Mayhew, Marek Šuppa +11
While multilingual language models promise to bring the benefits of LLMs to speakers of many languages, gold-standard evaluation benchmarks in most languages to interrogate these a…