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cs.CL2026
Flattery, Fluff, and Fog: Diagnosing and Mitigating Idiosyncratic Biases in Preference Models
Anirudh Bharadwaj, Chaitanya Malaviya, Nitish Joshi +1
Language models serve as proxies for human preference judgements in alignment and evaluation, yet they exhibit systematic miscalibration, prioritizing superficial patterns over sub…
cs.CL2025
Transformers Struggle to Learn to Search
Abulhair Saparov, Srushti Pawar, Shreyas Pimpalgaonkar +6
Search is an ability foundational in many important tasks, and recent studies have shown that large language models (LLMs) struggle to perform search robustly. It is unknown whethe…
cs.CL2024
LLMs Are Prone to Fallacies in Causal Inference
Nitish Joshi, Abulhair Saparov, Yixin Wang +1
Recent work shows that causal facts can be effectively extracted from LLMs through prompting, facilitating the creation of causal graphs for causal inference tasks. However, it is…